#Object detection in computer vision

[goodoldbandit]

Vision in Focus: The Art and Science of Computer Vision & Image Processing.

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in An insightful blog post on computer vision and image processing, highlighting its impact on medical diagnostics, autonomous driving, and security systems.
 Computer vision and image processing have reshaped the way we see and interact with the world. These fields power systems that read images, detect objects and analyze video…

[likhithageddam]

YOLO V/s Embeddings: A comparison between two object detection models

YOLO-Based Detection Model Type: Object detection Method: YOLO is a single-stage object detection model that divides the image into a grid and predicts bounding boxes, class labels, and confidence scores in a single pass. Output: Bounding boxes with class labels and confidence scores. Use Case: Ideal for real-time applications like autonomous vehicles, surveillance, and robotics. Example Models: YOLOv3, YOLOv4, YOLOv5, YOLOv8 Architecture

YOLO processes an image in a single forward pass of a CNN. The image is divided into a grid of cells (e.g., 13×13 for YOLOv3 at 416×416 resolution). Each cell predicts bounding boxes, class labels, and confidence scores. Uses anchor boxes to handle different object sizes. Outputs a tensor of shape [S, S, B*(5+C)] where: S = grid size (e.g., 13×13) B = number of anchor boxes per grid cell C = number of object classes 5 = (x, y, w, h, confidence) Training Process

Loss Function: Combination of localization loss (bounding box regression), confidence loss, and classification loss.

Labels: Requires annotated datasets with labeled bounding boxes (e.g., COCO, Pascal VOC).

Optimization: Typically uses SGD or Adam with a backbone CNN like CSPDarknet (in YOLOv4/v5). Inference Process

Input image is resized (e.g., 416×416). A single forward pass through the model. Non-Maximum Suppression (NMS) filters overlapping bounding boxes. Outputs detected objects with bounding boxes. Strengths

Fast inference due to a single forward pass. Works well for real-time applications (e.g., autonomous driving, security cameras). Good performance on standard object detection datasets. Weaknesses

Struggles with overlapping objects (compared to two-stage models like Faster R-CNN). Fixed number of anchor boxes may not generalize well to all object sizes. Needs retraining for new classes. Embeddings-Based Detection Model Type: Feature-based detection Method: Instead of directly predicting bounding boxes, embeddings-based models generate a high-dimensional representation (embedding vector) for objects or regions in an image. These embeddings are then compared against stored embeddings to identify objects. Output: A similarity score (e.g., cosine similarity) that determines if an object matches a known category. Use Case: Often used for tasks like face recognition (e.g., FaceNet, ArcFace), anomaly detection, object re-identification, and retrieval-based detection where object categories might not be predefined. Example Models: FaceNet, DeepSORT (for object tracking), CLIP (image-text matching) Architecture

Uses a deep feature extraction model (e.g., ResNet, EfficientNet, Vision Transformers). Instead of directly predicting bounding boxes, it generates a high-dimensional feature vector (embedding) for each object or image. The embeddings are stored in a vector database or compared using similarity metrics. Training Process Uses contrastive learning or metric learning. Common loss functions:

Triplet Loss: Forces similar objects to be closer and different objects to be farther in embedding space.

Cosine Similarity Loss: Maximizes similarity between identical objects.

Center Loss: Ensures class centers are well-separated. Training datasets can be either:

Labeled (e.g., with identity labels for face recognition).

Self-supervised (e.g., CLIP uses image-text pairs). Inference Process

Extract embeddings from a new image using a CNN or transformer. Compare embeddings with stored vectors using cosine similarity or Euclidean distance. If similarity is above a threshold, the object is recognized. Strengths

Scalable: New objects can be added without retraining.

Better for recognition tasks: Works well for face recognition, product matching, anomaly detection.

Works without predefined classes (zero-shot learning). Weaknesses

Requires a reference database of embeddings. Not good for real-time object detection (doesn’t predict bounding boxes directly). Can struggle with hard negatives (objects that look similar but are different).

Weaknesses

Struggles with overlapping objects (compared to two-stage models like Faster R-CNN). Fixed number of anchor boxes may not generalize well to all object sizes. Needs retraining for new classes. Embeddings-Based Detection Model Type: Feature-based detection Method: Instead of directly predicting bounding boxes, embeddings-based models generate a high- dimensional representation (embedding vector) for objects or regions in an image. These embeddings are then compared against stored embeddings to identify objects. Output: A similarity score (e.g., cosine similarity) that determines if an object matches a known category. Use Case: Often used for tasks like face recognition (e.g., FaceNet, ArcFace), anomaly detection, object re-identification, and retrieval-based detection where object categories might not be predefined. Example Models: FaceNet, DeepSORT (for object tracking), CLIP (image-text matching) Architecture

Uses a deep feature extraction model (e.g., ResNet, EfficientNet, Vision Transformers). Instead of directly predicting bounding boxes, it generates a high-dimensional feature vector (embedding) for each object or image. The embeddings are stored in a vector database or compared using similarity metrics. Training Process Uses contrastive learning or metric learning. Common loss functions:

Triplet Loss: Forces similar objects to be closer and different objects to be farther in embedding space.

Cosine Similarity Loss: Maximizes similarity between identical objects.

Center Loss: Ensures class centers are well-separated. Training datasets can be either:

Labeled (e.g., with identity labels for face recognition).

Self-supervised (e.g., CLIP uses image-text pairs). Inference Process

Extract embeddings from a new image using a CNN or transformer. Compare embeddings with stored vectors using cosine similarity or Euclidean distance. If similarity is above a threshold, the object is recognized. Strengths

Scalable: New objects can be added without retraining.

Better for recognition tasks: Works well for face recognition, product matching, anomaly detection.

Works without predefined classes (zero-shot learning). Weaknesses

Requires a reference database of embeddings. Not good for real-time object detection (doesn’t predict bounding boxes directly). Can struggle with hard negatives (objects that look similar but are different).

[image-classification]

Image Classification vs Object Detection

Image classification, object detection, object localization — all of these may be a tangled mess in your mind, and that's completely fine if you are new to these concepts. In reality, they are essential components of computer vision and image annotation, each with its own distinct nuances. Let's untangle the intricacies right away.We've already established that image classification refers to assigning a specific label to the entire image. On the other hand, object localization goes beyond classification and focuses on precisely identifying and localizing the main object or regions of interest in an image. By drawing bounding boxes around these objects, object localization provides detailed spatial information, allowing for more specific analysis.

Object detection on the other hand is the method of locating items within and image assigning labels to them, as opposed to image classification, which assigns a label to the entire picture. As the name implies, object detection recognizes the target items inside an image, labels them, and specifies their position. One of the most prominent tools to perform object detection is the “bounding box” which is used to indicate where a particular object is located on an image and what the label of that object is. Essentially, object detection combines image classification and object localization.

[fairsweetlonging]

svsss horror game au where shen yuan is first in line to buy the pidw-inspired rpg where you play as a wandering cultivator with amnesia, and are taken in by the cang qiong sect during the head disciple days of the last peak lord generation.

because pidw knows its audience, a large part of the marketing was focused on the romance and action aspect of the game, with additional lore from deleted novel scenes—how could shen yuan not buy this game? maybe the peak lords will finally be more than props in the background! the romance aspect seems to be at least somewhat tastefully done, if he can trust the leaks, with more emotional depth than fetish fulfillment (shen yuan swears that if there is even one unskippable cutscene of some peak lord's feet he's going to chuck his computer out the window).

shen yuan customizes his character, going all out on the clichés because why not, giving him white hair and peerless beauty and all the characteristics of an A+ wife (beauty is power in pidw), actually excited to play the game. the first part is standard, you wake up in a barn with amnesia, only a sword and some items to your name, and have to do some tutorial quests to get used to the game mechanics. it's simple enough. eventually, you end up in a village that shen yuan is certain is possessed, because all the NPC's act very unnatural and strange, and it's pretty unsettling. here, the player is supposed to meet the cang qiong head disciples on their own quest, who naturally think the player is the most interesting person they've ever seen, a super special cultivator, and will take him in because the player is the most coveted character in the universe (apart from luo binghe, that is).

of course, before shen yuan can get very far, he ends up being transmigrated into the game as his own character. it could be way worse: he's a cultivator, peerlessly beautiful, destined to be picked up by the most prestigious sect, and has his own protagonist halo of sorts. he's honestly pretty excited about this

until he finds out that the marketing heavily downplayed the horror elements of the game.

shen yuan is calmly eating a meal in an inn of the village, waiting for the next quest point to start, when suddenly,

[ system notification ]

"you are being observed"

observation level: ???

entity classification: unknown

engagement protocol: do not acknowledge

right after, the windows go dark, not closed or shuttered, dark, as if something large has just leaned against the side of the building. no one else acknowledges this.

shen yuan shakes it off. it's just a game, it's... ambiance, that's all. build up.

he walks through the streets of the town, using his low-level talismans to try and find traces of the entity he's supposed to defeat or uncover to complete the quest. he pauses beside a broken cart, one of its wheels is half-sunk in the mud. the system pings again.

[ system notification ]

"it's behind you."

note: do not turn around.

(option to suppress message: [ ] not recommended)

the street is utterly silent. a prickle begins at the base of his skull. something is there. some deep animalistic part of him is already screaming not to look.

it disappears. he earns 5 survival points. he hopes he won't have to earn any more.

later that night, shen yuan looks for shelter, finding an old shrine visible from the road, just at the side of town. he steps inside and sees old incense sticks, some forgotten offerings. it's simple, but dry. it will do.

he crosses the threshold—

[ mission triggered ]

mission objective: hide

time limit: unknown

condition to complete: remain unnoticed

footsteps crunch in the leaves outside. every nerve in him goes rigid—not human.

too heavy. uneven. it's coming.

shen yuan ducks behind the offering table, body pressed flat against the ground. he slows his breathing, barely daring to blink. a screen in his peripheral vision blinks to life.

[ environmental mechanic activated ]

microphone mode: ON

sound detection level: HIGH

a semi-transparent sound meter appears. with every shaky breath, the bar pulses red. shen yuan clamps his hands over his mouth.

something passes, just beyond the shrine's opening. large. the system does not count down. there is no timer. the floor boards moan faintly beneath a ponderous weight, something drags across the ground.

shen yuan forces his body still, trembling so hard it hurts his teeth.

it leaves. the system congratulates him for surviving. it doesn't tell him what he just survived.

it's a relief when the head disciples of cang qiong show up, and the story delves into romantic cliches and relationship prompts. he gets to see liu qingge shirtless. shen qingqiu is typical tsundere. yue qingyuan is the soft gentle type. shang qinghua acts... off. he isn't what shen yuan thought he would be, less cunningly charming, more, well. nervous. of all the head disciples, he's the only one who actually seems like he doesn't want shen yuan to be here, always looking around.

like he knows shen yuan didn't come alone.

more instances like this occur. one moment, he's farming reputation points and relationship points with the other characters, doing quests and gathering memory fragments that will help unlock the player's backstory, the next, the system seems determined to make the game hell.

it always comes out of nowhere

[ system update ]

"warning: your heartbeat has been logged by another entity."

would you like to mute heartbeat tracking?

[ ] yes

[ ] no

[ ] it's too late.

he can never figure out what's following him, what that creature from the village is, but it's always there. no one else seems to notice, not a single talisman or ward can stop or detect it.

it comes even when he's in bed, still faintly blushing from a wife-plot equivalent where he fell from a ladder and was caught in wei qingwei's arms. he got to pet the pangolins too!

he's just about to fall asleep when the system pings:

[ mission objective: survive until dawn ]

hint: do not scream

somewhere beneath the floorboards under his bed, something begins scraping. like claws trying to memorize the layout of the house from below. shen yuan doesn't dare move. sleep never comes that night.

*

he can farm intelligence points by attending classes, and being the monster and plant nerd he is, qian cao peak is his first choice (it's either that, being beat up by bai zhan disciples that aren't even liu qingge, or running into shen qingqiu).

in the middle of a lesson on demonic poisons, the system pings quietly

[ system message ]

"one of the bodies in the infirmary is not a body"

objective: don't lose sight of it

shen yuan turns his head, slowly, to the curtained recovery beds along the wall. the curtain on the last one is slightly open.

it wasn't before.

mu qingfang continues speaking. shen yuan doesn't dare to look away.

*

one day, the thing starts to catch up

[ mission failure ]

"the sound you made has been registered"

estimated proximity: 00:00:17

do you want to run?

[ ] yes (not recommended)

[ ] no (not recommended)

*

[ emergency notice ]

"you were seen"

objective: hide

time limit: expired

success rate: 2%.

do you want to proceed?

[ ] yes

[ ] yes

*

[ achievement unlocked: it found you anyway ]

*

anyway, can you tell i had fun with the horror prompts? ^_^

i just have sooooo many ideas for the player's backstory, where it seems the character is just a blank slate for the player to project themselves onto, but there is so much more to them than you think. im also having loads of fun with the creature that follows the player around, i love making it as disturbing as possible.

mild spoiler: the creature is real and connected to the player. other characters can't detect or interact with it, but it's slowly growing stronger. shang qinghua is, of course, airplane, and as he was directly involved with the production of this game, he knew that as soon as an OC showed up, that thing wouldn't be far behind.

also, i love the idea of shang qinghua being stuck in a dating simulator as one of the options to romance. now shang qinghua has to play along with his own cringy cliche meetcutes, like showing the player around, flirting with the player, and generally playing the role of suave administrator with a dark secret (he's terrible at it). he had to do the "there's an eyelash on your cheek allow me" move on the player (shen yuan), and almost cringed out of his own skin. though, shang qinghua is the only one who can properly emphasize with the player, because he actually knows what horrid creature is stuck to him and what kind of horror scenarios the player has to go through (accidental cumplane? it's more likely than you think).

it's a bit of a mindfuck too, because shang qinghua can't tell whether the player is also a transmigrator, a puppet controlled by someone from another dimension, or a fleshed out OC of the system. he's also not allowed to ask, so it remains ambiguous. until, of course, they find out they're transmigrators and shen yuan has to deal with the fact he almost romanced airplane.

shen yuan makes a joke about defeating the creature with the power of love. shang qinghua says he wished it was that easy.

[mustafabukulmez-blog]

Bilgisayar Görüsü Nedir?

Merhaba. Bu yazımızda Bilgisayar Görüsü Nedir?  sorunun cevabına bakacağız. Bu konu elbette yeni bir konu değil ancak başlattığım Üretim ve Yönetim Sistemlerinin Tarihsel Gelişimi serisi için devam niteliğinde olan bir konu olduğu için yazmak istedim. Aynı zamanda videosu da gelecektir. Üretim ve Yönetim Sistemlerinin Tarihsel Gelişimi youtube podcast seri için buraya yazı serim…

[unidot]

Some Fade Valorant headcanons from my twt

-Since her powers are not mind reading and not clear, she also uses her psychology knowledge to base assumptions on people and use their fears against them more affectively

-Fidgets with her hands a lot. She either uses an object to keep her hands busy or uses her nightmare tendrils

-Doing henna and drawing are some kind of meditation for her. They help her to keep herself grounded when the nightmares and visions get especially bad

-She prefers bitter coffee

-Born in the city of Bursa, later moved to İstanbul

-She doesnt have a cat of her own, mostly takes care of street cats

-She loves homemade food but is not that good at cooking so she mostly goes to restaurants that makes homemade like food

-She is around 172 cm (5'8") tall and is 27-28 years old

-since its confirmed that the nightmare is not a seperate entity, the prowlers act on her most basic emotions deep down (playing with people she likes, hissing at people she dislikes etc)

-She cant shut down her powers because they work like a 6th sense in a way. She constantly feels the fear and discomfort around her but choses to not focus on it

-Designed her own nazar symbol

-She is really bad at singing

-likes photography and she is good at it thanks to needing to take a lot of photos in her job

-Knows hacking because she hacked into Cypher's computers and compiled all the information on the protocol without being detected

-she sometimes plays chess with Cypher

-Her favorite color is blue (color of nazar, her vest and her ult)

-does coffee fortune telling for her friends

-her favourite food is mantı

-didnt really had a good education but has a lot of knowledge on stuff thanks to reading a lot and doing a lot of research on stuff she is curious about

-She was really skinny when she joined vp (mostly because she didnt really took good care of herself as she did research for her blackmail attack) gained some fat and muscle after vp's food and training

-she learned some German in middleschool and highschool. With that vp has 2 agents who both knows Turkish and German (Kj being half Turkish from her mothers side)

-Omen and her dms are full of cat videos they found

-her prowlers name is Karabasan

-her favorite book genres are books that explore the human mind or detective books

-she tans easily

-she dislikes swimming. Prefers to read a book on the beach

-she is generally tidy but can get messy when she is focused on a mission. Her desk especially becomes a mess

-she smells like coffee and burnt sages

-she doesn't really care about other agents' opinions on her. She is still friendly sometimes and civil to them, but if they don't forgive her, she honestly doesn't give a shit. And agents that still dislike her are mostly civil towards her

-if she is feeling down, she often goes on walks outside. Helps to clear her mind

-its hard for her to care for something or someone. But if she does, she cares so much

-she has a motorcycle back home. It's easier and more efficient for her since Istanbul traffic can be hell

-she is great at gambling or games like gambling since she is observant and can just feel the peoples fear or anxiety of losing

-she sometimes falls asleep (passes out) on random places if she hasn't slept in a really long time

-Omen knit a sweater and a scarf for her

-she gets along with Harbor really well. They share books and talk about their experiences with working in Realm while drinking tea or coffee

-she can really relate to Neon with not being able to control her powers fully and that affecting her life and relationships. She doesn't admit it, though

-she plays backgammon with Cypher and Harbor

-she spends a lot of time and effort on her "messy" appearance

-she is one of the busiest agents. She gets a lot of assignments (mostly intel work)

-she knows all of the agents' most secrets and fears but she honestly couldnt care less. Your secret is safe with her (If you are on her good side)

-she prefers to use a Phantom than a Vandal

-she finds Dizzy cute because she looks like a sleepy kitten

-she is still secretly salty about KAY/O catching her

-she and Skye dont really like eachother that well but they see eachother often during the early mornings (Skye going for a morning run and Fade still not sleeping) and Skye's tiger and Fade's prowlers likes to play so they end up seeing eachother more than they would like

-Used to go to clubs and bars often. Mostly to stay awake and keep her mind busy

-She is actually kinda rich. Her bounty hunter job paid her well

-Secretly wants Neon's black cat plushie but would never admit it

-she is really great at reading people but she is bad at interacting positively towards them. Her compliments or her comforting words can be awkward or just not appropriate

-other than cats, one of the other animals she really likes is octopuses

-she and Chamber trade expensive coffee

-she has a lot of scars on her body

-she is not that psychically strong compared to other agents

-she has high alcohol tolerance

-names all the cats she looks after on the streets. Either gives them cute names or just normal human names

-she is superstitious. Mostly about nazar

-her hand writting is really messy

-she was born left handed but she is now ambidextrous

[compneuropapers]

Interesting Papers for Week 22, 2025

Reaching Distance Influences Perceptual Decisions. Assarioti, E. E., van Beers, R. J., Smeets, J. B. J., & van Wijk, B. C. M. (2025). European Journal of Neuroscience, 61(3).

Detection of latent brain states from spontaneous neural activity in the amygdala. Aucoin, A., Lin, K. K., & Gothard, K. M. (2025). PLOS Computational Biology, 21(2), e1012247.

Repeated stress gradually impairs auditory processing and perception. Bisharat, G., Kaganovski, E., Sapir, H., Temnogorod, A., Levy, T., & Resnik, J. (2025). PLOS Biology, 23(2), e3003012.

A neuronal code for object representation and memory in the human amygdala and hippocampus. Cao, R., Brunner, P., Chakravarthula, P. N., Wahlstrom, K. L., Inman, C., Smith, E. H., Li, X., Mamelak, A. N., Brandmeir, N. J., Rutishauser, U., Willie, J. T., & Wang, S. (2025). Nature Communications, 16, 1510.

Contextual neural dynamics during time perception in the primate ventral premotor cortex. Díaz, H., Bayones, L., Alvarez, M., Andrade-Ortega, B., Valero, S., Zainos, A., Romo, R., & Rossi-Pool, R. (2025). Proceedings of the National Academy of Sciences, 122(6), e2420356122.

Attention Rhythmically Shapes Sensory Tuning. Galas, L., Donovan, I., & Dugué, L. (2025). Journal of Neuroscience, 45(7), e1616242024.

A detailed theory of thalamic and cortical microcircuits for predictive visual inference. George, D., Lázaro-Gredilla, M., Lehrach, W., Dedieu, A., Zhou, G., & Marino, J. (2025). Science Advances, 11(6).

Time Course of Orientation Ensemble Representation in the Human Brain. Gong, X., He, T., Wang, Q., Lu, J., & Fang, F. (2025). Journal of Neuroscience, 45(7), e1688232024.

Comprehensive exploration of visual working memory mechanisms using large-scale behavioral experiment. Huang, L. (2025). Nature Communications, 16, 1383.

Hippocampal damage disrupts the latent decision-making processes underlying approach-avoidance conflict processing in humans. Le Duc, W., Butler, C. R., Argyropoulos, G. P. D., Chu, S., Hutcherson, C., Ruocco, A. C., Ito, R., & Lee, A. C. H. (2025). PLOS Biology, 23(2), e3003033.

The geometry of correlated variability leads to highly suboptimal discriminative sensory coding. Livezey, J. A., Sachdeva, P. S., Dougherty, M. E., Summers, M. T., & Bouchard, K. E. (2025). Journal of Neurophysiology, 133(1), 124–141.

Fixational eye movements as active sensation for high visual acuity. Nghiem, T.-A. E., Witten, J. L., Dufour, O., Harmening, W. M., & Azeredo da Silveira, R. (2025). Proceedings of the National Academy of Sciences, 122(6), e2416266122.

Inferring when to move. Parr, T., Oswal, A., & Manohar, S. G. (2025). Neuroscience & Biobehavioral Reviews, 169, 105984.

Modular Maximization Theory: A functional account of economic behavior in laboratory animal models with applications to drug-seeking behavior. Sanabria, F., Gildea, M., Gutiérrez, B., Santos, C., & Hibshman, A. (2025). Neuroscience & Biobehavioral Reviews, 169, 106010.

Hexagons all the way down: grid cells as a conformal isometric map of space. Schøyen, V. S., Beshkov, K., Pettersen, M. B., Hermansen, E., Holzhausen, K., Malthe-Sørenssen, A., Fyhn, M., & Lepperød, M. E. (2025). PLOS Computational Biology, 21(2), e1012804.

Active vision in freely moving marmosets using head-mounted eye tracking. Singh, V. P., Li, J., Dawson, K., Mitchell, J. F., & Miller, C. T. (2025). Proceedings of the National Academy of Sciences, 122(6), e2412954122.

The receptive field construction of midget ganglion cells in primate retina. Somaratna, M. A., & Freeman, A. W. (2025). Journal of Neurophysiology, 133(1), 268–285.

Contributions of Attention to Learning in Multidimensional Reward Environments. Wang, M. C., & Soltani, A. (2025). Journal of Neuroscience, 45(7), e2300232024.

A biological model of nonlinear dimensionality reduction. Yoshida, K., & Toyoizumi, T. (2025). Science Advances, 11(6).

Computational mechanism underlying switching of motor actions. Zhong, S., Pouratian, N., & Christopoulos, V. (2025). PLOS Computational Biology, 21(2), e1012811.

[animacion-marina]

How to resize image without losing quality

Supervised Learning: KNN is a supervised learning algorithm, meaning it learns from labeled data to make predictions. Instance-Based Learning: KNN is also considered an instance-based or lazy learning algorithm because it stores the entire training dataset and performs computations only when making predictions. 

Use Java Maven Project For Image Resize

media stands out as the best tool for resizing pictures. With its user-friendly interface and advanced resizing capabilities, Shrink. media allows you to adjust your pictures' dimensions and file sizes while maintaining excellent visual quality.

Boof CV for Java Image Processing

Java CV for Java Image Processing

ImageJ for Java Image Processing

Boof CV is a comprehensive library designed for real-time computer vision and image processing applications. It focuses on providing simple and efficient algorithms for tasks such as image enhancement, feature detection, and object tracking.

[materialsscienceandengineering]

Novel technique for observing atomic-level changes could unlock potential of quantum materials

A research team led by the Department of Energy's Oak Ridge National Laboratory has devised a unique method to observe changes in materials at the atomic level. The technique opens new avenues for understanding and developing advanced materials for quantum computing and electronics. The paper is published in the journal Science Advances. The new technique, called the Rapid Object Detection and Action System, or RODAS, combines imaging, spectroscopy and microscopy methods to capture the properties of fleeting atomic structures as they form, providing unprecedented insights into how material properties evolve at the smallest scales. Traditional approaches combining scanning transmission electron microscopy, or STEM, with electron energy loss spectroscopy, or EELS, have been limited because the electron beam can change or degrade the materials being analyzed. That dynamic often causes scientists to measure altered states rather than the intended material properties. RODAS overcomes the limitation and also integrates the system with dynamic computer-vision-enabled imaging, which uses real-time machine learning.

Read more.

[coderacha]

2023.08.31

i have no idea what i'm doing!

learning computer vision concepts on your own is overwhelming, and it's even more overwhelming to figure out how to apply those concepts to train a model and prepare your own data from scratch.

context: the public university i go to expects the students to self-study topics like AI, machine learning, and data science, without the professors teaching anything TT

i am losing my mind

based on what i've watched on youtube and understood from articles i've read, i think i have to do the following:

data collection (in my case, images)

data annotation (to label the features)

image augmentation (to increase the diversity of my dataset)

image manipulation (to normalize the images in my dataset)

split the training, validation, and test sets

choose a model for object detection (YOLOv4?)

training the model using my custom dataset

evaluate the trained model's performance

so far, i've collected enough images to start annotation. i might use labelbox for that. i'm still not sure if i'm doing things right 🥹

if anyone has any tips for me or if you can suggest references (textbooks or articles) that i can use, that would be very helpful!

[rainbowxocs]

Name: James DuPont.

Alt Names: C.A.T, Pluto, Charon, Jane Doe.

Special Titles: Dr. James DuPont, Grandmaster, God Killer, Cat Burglar, EOD, Lieutenant Colonel, Sharpshooter, False God, The Star, Narrator.

Old Titles: Knight, God of Duality, God of Judgement, God of Eternity, God of Chaos, Servant, Empiric.

Username: @kitty9lives

Nicknames: Bad Omen, Kit Cat, Cat Boy, My Rose, My Star, Stray, Blue Bird, Kitty, Chaton, Bunny, Phoenix, Holmes, My Beloathed, Final Girl, The Prophet, Schrodingers Cat.

Chronological Age: 4.5 Billion.

Vessel Age: 605.

Age: 45.

Pronouns: Switches between He, She, and They. Depending on what gender he is that day. (Switches between il or elle in French)

Sexuality: Gay.

Gender: Genderfluid, Catgender.

Base Species: Starling.

Current Species: “Human” (Pure Hybrid)

Hybrid Info: (Sphinx, Litch, Witch.)

Disorders: CPTSD, Autism, Insomnia, Selective Mutism, Night Terrors, BPD, Anorexia.

Physical Disabilities: Blind, Deaf (Has a Cochlear Implant), Ambulatory Wheelchair User (Occasionally uses crutches or a cane as well), Has two arm prosthetics and two leg prosthetics, Chronic Pain.

Recovering Addictions: Alcohol, Weed, Nicotine (Cigarettes), LSD, Self Harm.

Religion: Pagan.

Job: Professional Villain, Chemist.

Degree: M.D, Chemistry, Robotics, Computer Science.

Lives in: NYC, New York, 2307.

Languages: French, English, Hindi, ASL, LSF, Spanish, Italian, German, Danish, Dutch.

Height: 5’7”.

Ethnicity: French, Portuguese.

Accent: Brooklyn Accent with a hint of French.

Other Form: Purple Goop.

Animal Form: Giant Purple Isopod.

Spirit Form: Headless, Covered in Roses.

Spirit Level: Acceptance.

Powers: Reanimating, Creation Magic, Death Magic, Prophetic Visions, Judgement, Potions, Alchemy, Shapeshifting, Strings, Pandora’s Box, Lightning Magic, Technology Manipulation, Lie Detection, Time Magic, Forbidden Fruit.

Tech: Holograms, Robotic Minions, Smoke Bombs, Paint Bombs, Teleporters, Lock Picks, Lazers.

Weapons: Sword, Pistols, Sniper Rifle, Bombs, Rocks, Scissors, Various Witchcraft Supplies such as salt, wards, etc, Scissors.

Also Can Use: Muskets, Rifled Muskets, Rifles,

Wand: Uses his hands.

Alignment: Chaotic Good.

Text Color: Purple, Sometimes Black.

Main Animal: Cat.

Main Hobbies: Reading, Video Games, Sculpting, Yugioh, Violin, Otamatone, Puzzles, Robotics, Scientific Experimentation, Coding, Chess, Letter Making, Tambourine, Photography, Flute.

Favorite Drinks: Peppermint Tea, Coffee, Classic Boba.

Favorite Snacks: Queso, Saltines, Apples.

Favorite Meals: Garlic Bread, Dino Nuggets and Fries, Mushroom and Olives Pizza, Pancakes, Veal Stew, Pigs in a Blanket, Hot Dogs, Tuna, Chicken Wings, Mac and Cheese, Ham Sandwiches, Maki Rolls, Sashimi, Bagels.

Favorite Candy: Pez, Oreos.

Favorite Dessert: Gingerbread Cookies, Frosted Sugar Cookies, Birthday Icecream.

Favorite Flower: Roses, Purple Forget Me Not.

Scent: Roses.

Handedness: Left Handed.

Blood Color: Bronze, Sometimes Red.

Awareness: Very Aware. (Effect: Negative.)

Birthday: December 1st 1701. (Sagittarius.)

Theme:

Playlist:

Fun Facts: He is always wearing cat patterns and tends to have toe beans on his shoes and gloves.

Special Interests: Technology, Robotics, Chemistry, The Sims, The Path, Sailor Moon, Disney Fairies, The Owl House, Steven Universe, FNAF, Kitty Love: Way to Look for Love.

Stims: Tangles, Cat Noises, Lazer Pointer, Yarn, Pressure Stims.

Stimboard: COMING SOON.

Moodboard: COMING SOON.

Fashion Board: COMING SOON.

Comfort Objects: Wedding Ring with Gold Band and Amethyst, Journal, Furby, Freddy Plush, Old Cat Plush, Gloomy Bear, Fuggler.

Family: Unknown Birth Parents.

Friends: Joan (Henchman.), Kriston.

Romance: Jonah Francois, Aditya Ravi. (Spouses.)

Enemies: Jonah Francois (Mortal Enemy), Michael Ansley.

Patrons: Bastet, Santa Muerte, Hecate.

Pets: Eyeball (Robot), Chain Chomp (Roomba), Mr Terminator (Black and White Cat),

Reincarnations: 𒆠𒋫 (Kita), חַוָּה (Eve), Πανδώρα (Pandora), दिया (Diya), Juliet, Pied Piper, Other Unknown Reincarnations.

Brief Personality: James is a bit of an enigma. He doesn’t get close to many people, often his ramblings about taking over the world push people away. However if you are persistent, he will warm up to you like a stray. He is incredibly intelligent, and also very very VERY stubborn. But he is incredibly loyal to the people he loves. If you are able to gain his trust he would let the world burn for you, without any hesitation.

Brief Backstory: [COMING SOON]

[writingquestionsanswered]

Hi, I'm trying to come up with a good friend/assistant for a Detective I'm writing. What's a good job/occupation that could help them beforehand? I don't want to do doctor/medical as that's a bit played out and wouldn't work into her story.

Occupation of Detective's Helper

First, it depends on what type of detective they are, because there are detectives, private detectives, and private investigators--and private investigators are sometimes referred to as detectives. Detectives work with a law enforcement agency, private detectives are employed by law firms, insurance companies, or corporations, but they are bound by the law like regular detectives. Private investigators, on the other hand, can be hired by anyone in the private sector and although they're bound to the law to the same degree as any other citizen, they're more likely to do things detectives can't due to being law enforcement/employed by a high profile organization.

For law enforcement detectives, any number of people within the law enforcement agency might assist them. You would need to research the specific type of law enforcement agency, see what their structure is, and find out the different people who commonly help investigate cases.

If you're going with a private detective or private investigator, although it's almost as played out as medical assistants, you could go the techie route... either the computer whiz who can "zoom and enhance" on gritty video images and triangulate last known whereabouts using cell phone pings. Or, the person who the detective goes to for the latest technical gear... the night vision goggles, the covert listening devices, and the cameras that look like random objects.

Other options for private detectives and private investigators:

-- If the they go undercover a lot, they might have someone who helps them with fake IDs, fake documents, etc.

-- Although it's in the same ballpark as the medical stuff, a scientist or other lab worker who analyzes finger prints, DNA, etc.

-- An expert in something the private detective/private investigator deals with often, like gangs/factions, the underbelly of the city, political connections, etc.

-- Someone who can pull strings in high places, like the D.A.'s assistant or the mayor's summer intern, or even a local politician.

-- Someone who can get any information about anything... they just know the right places to look/ask.

-- Someone in the media who has a lot of connections or has their finger on the pulse of the city.

-- If the detective/investigator works in a place where they're going to encounter people who speak a different language, or a lot of different languages, they might have a multilingual interpreter who can translate for them.

That's all I can think of off the top of my head, but keep an eye on the comments in case anyone else has ideas!

•••••••••••••••••••••••••••••••••

I’ve been writing seriously for over 30 years and love to share what I’ve learned. Have a writing question? My inbox is always open!

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[teqful]

How-To IT

Topic: Core areas of IT

1. Hardware

• Computers (Desktops, Laptops, Workstations)

• Servers and Data Centers

• Networking Devices (Routers, Switches, Modems)

• Storage Devices (HDDs, SSDs, NAS)

• Peripheral Devices (Printers, Scanners, Monitors)

2. Software

• Operating Systems (Windows, Linux, macOS)

• Application Software (Office Suites, ERP, CRM)

• Development Software (IDEs, Code Libraries, APIs)

• Middleware (Integration Tools)

• Security Software (Antivirus, Firewalls, SIEM)

3. Networking and Telecommunications

• LAN/WAN Infrastructure

• Wireless Networking (Wi-Fi, 5G)

• VPNs (Virtual Private Networks)

• Communication Systems (VoIP, Email Servers)

• Internet Services

4. Data Management

• Databases (SQL, NoSQL)

• Data Warehousing

• Big Data Technologies (Hadoop, Spark)

• Backup and Recovery Systems

• Data Integration Tools

5. Cybersecurity

• Network Security

• Endpoint Protection

• Identity and Access Management (IAM)

• Threat Detection and Incident Response

• Encryption and Data Privacy

6. Software Development

• Front-End Development (UI/UX Design)

• Back-End Development

• DevOps and CI/CD Pipelines

• Mobile App Development

• Cloud-Native Development

7. Cloud Computing

• Infrastructure as a Service (IaaS)

• Platform as a Service (PaaS)

• Software as a Service (SaaS)

• Serverless Computing

• Cloud Storage and Management

8. IT Support and Services

• Help Desk Support

• IT Service Management (ITSM)

• System Administration

• Hardware and Software Troubleshooting

• End-User Training

9. Artificial Intelligence and Machine Learning

• AI Algorithms and Frameworks

• Natural Language Processing (NLP)

• Computer Vision

• Robotics

• Predictive Analytics

10. Business Intelligence and Analytics

• Reporting Tools (Tableau, Power BI)

• Data Visualization

• Business Analytics Platforms

• Predictive Modeling

11. Internet of Things (IoT)

• IoT Devices and Sensors

• IoT Platforms

• Edge Computing

• Smart Systems (Homes, Cities, Vehicles)

12. Enterprise Systems

• Enterprise Resource Planning (ERP)

• Customer Relationship Management (CRM)

• Human Resource Management Systems (HRMS)

• Supply Chain Management Systems

13. IT Governance and Compliance

• ITIL (Information Technology Infrastructure Library)

• COBIT (Control Objectives for Information Technologies)

• ISO/IEC Standards

• Regulatory Compliance (GDPR, HIPAA, SOX)

14. Emerging Technologies

• Blockchain

• Quantum Computing

• Augmented Reality (AR) and Virtual Reality (VR)

• 3D Printing

• Digital Twins

15. IT Project Management

• Agile, Scrum, and Kanban

• Waterfall Methodology

• Resource Allocation

• Risk Management

16. IT Infrastructure

• Data Centers

• Virtualization (VMware, Hyper-V)

• Disaster Recovery Planning

• Load Balancing

17. IT Education and Certifications

• Vendor Certifications (Microsoft, Cisco, AWS)

• Training and Development Programs

• Online Learning Platforms

18. IT Operations and Monitoring

• Performance Monitoring (APM, Network Monitoring)

• IT Asset Management

• Event and Incident Management

19. Software Testing

• Manual Testing: Human testers evaluate software by executing test cases without using automation tools.

• Automated Testing: Use of testing tools (e.g., Selenium, JUnit) to run automated scripts and check software behavior.

• Functional Testing: Validating that the software performs its intended functions.

• Non-Functional Testing: Assessing non-functional aspects such as performance, usability, and security.

• Unit Testing: Testing individual components or units of code for correctness.

• Integration Testing: Ensuring that different modules or systems work together as expected.

• System Testing: Verifying the complete software system’s behavior against requirements.

• Acceptance Testing: Conducting tests to confirm that the software meets business requirements (including UAT - User Acceptance Testing).

• Regression Testing: Ensuring that new changes or features do not negatively affect existing functionalities.

• Performance Testing: Testing software performance under various conditions (load, stress, scalability).

• Security Testing: Identifying vulnerabilities and assessing the software’s ability to protect data.

• Compatibility Testing: Ensuring the software works on different operating systems, browsers, or devices.

• Continuous Testing: Integrating testing into the development lifecycle to provide quick feedback and minimize bugs.

• Test Automation Frameworks: Tools and structures used to automate testing processes (e.g., TestNG, Appium).

19. VoIP (Voice over IP)

VoIP Protocols & Standards

• SIP (Session Initiation Protocol)

• H.323

• RTP (Real-Time Transport Protocol)

• MGCP (Media Gateway Control Protocol)

VoIP Hardware

• IP Phones (Desk Phones, Mobile Clients)

• VoIP Gateways

• Analog Telephone Adapters (ATAs)

• VoIP Servers

• Network Switches/ Routers for VoIP

VoIP Software

• Softphones (e.g., Zoiper, X-Lite)

• PBX (Private Branch Exchange) Systems

• VoIP Management Software

• Call Center Solutions (e.g., Asterisk, 3CX)

VoIP Network Infrastructure

• Quality of Service (QoS) Configuration

• VPNs (Virtual Private Networks) for VoIP

• VoIP Traffic Shaping & Bandwidth Management

• Firewall and Security Configurations for VoIP

• Network Monitoring & Optimization Tools

VoIP Security

• Encryption (SRTP, TLS)

• Authentication and Authorization

• Firewall & Intrusion Detection Systems

• VoIP Fraud DetectionVoIP Providers

• Hosted VoIP Services (e.g., RingCentral, Vonage)

• SIP Trunking Providers

• PBX Hosting & Managed Services

VoIP Quality and Testing

• Call Quality Monitoring

• Latency, Jitter, and Packet Loss Testing

• VoIP Performance Metrics and Reporting Tools

• User Acceptance Testing (UAT) for VoIP Systems

Integration with Other Systems

• CRM Integration (e.g., Salesforce with VoIP)

• Unified Communications (UC) Solutions

• Contact Center Integration

• Email, Chat, and Video Communication Integration

[brownbiochemist]

Face Blur Technology in Public Surveillance: Balancing Privacy and Security

As surveillance technology continues to evolve, so do concerns about privacy. One solution that addresses both the need for public safety and individual privacy is face blur technology. This technology automatically obscures individuals’ faces in surveillance footage unless there’s a legitimate need for identification, offering a balance between security and personal data protection.

Why Do We Need Face Blur Technology?

Surveillance systems are increasingly used in public spaces, from streets and parks to malls and airports, where security cameras are deployed to monitor activities and prevent crime. However, the widespread collection of images from public spaces poses serious privacy risks. Personal data like facial images can be exploited if not properly protected. This is where face blur technology comes in. It reduces the chances of identity theft, unwarranted surveillance, and abuse of personal data by ensuring that identifiable information isn’t exposed unless necessary. Governments, businesses, and institutions implementing face blur technology are taking a step toward more responsible data handling while still benefiting from surveillance systems (Martinez et al., 2022).

Key Technologies Behind Face Blur

Face blur technology relies on several key technologies:

Computer Vision: This technology enables systems to detect human faces in images and videos. Using machine learning algorithms, cameras or software can recognize faces in real-time, making it possible to apply blurring instantly.

Real-life example: Google’s Street View uses face blur technology to automatically detect and blur faces of people captured in its 360-degree street imagery to protect their privacy.

Artificial Intelligence (AI): AI plays a crucial role in improving the accuracy of face detection and the efficiency of the blurring process. By training models on large datasets of human faces, AI-powered systems can differentiate between faces and non-facial objects, making the blurring process both accurate and fast (Tao et al., 2023).

Real-life example: Intel’s OpenVINO toolkit supports AI-powered face detection and blurring in real-time video streams. It is used in public surveillance systems in places like airports and transportation hubs to anonymize individuals while maintaining situational awareness for security teams.

Edge Computing: Modern surveillance systems equipped with edge computing process data locally on the camera or a nearby device rather than sending it to a distant data center. This reduces latency, allowing face blurring to be applied in real-time without lag.

Real-life example: Axis Communications’ AXIS Q1615-LE Mk III surveillance camera is equipped with edge computing capabilities. This allows for face blurring directly on the camera, reducing the need to send sensitive video footage to a central server for processing, enhancing privacy.

Encryption: Beyond face blur, encryption ensures that any data stored from surveillance cameras is protected from unauthorized access. Even if footage is accessed by someone without permission, the identity of individuals in the footage remains obscured.

Real-life example: Cisco Meraki MV smart cameras feature end-to-end encryption to secure video streams and stored footage. In conjunction with face blur technologies, these cameras offer enhanced privacy by protecting data from unauthorized access.

How Does the Technology Work?

The process of face blurring typically follows several steps:

Face Detection: AI-powered cameras or software scan the video feed to detect human faces.

Face Tracking: Once a face is detected, the system tracks its movement in real-time, ensuring the blur is applied dynamically as the person moves.

Face Obfuscation: The detected faces are then blurred or pixelated. This ensures that personal identification is not possible unless someone with the proper authorization has access to the raw footage.

Controlled Access: In many systems, access to the unblurred footage is restricted and requires legal or administrative permission, such as in the case of law enforcement investigations (Nguyen et al., 2023).

Real-life example: The Genetec Omnicast surveillance system is used in smart cities and integrates privacy-protecting features, including face blurring. Access to unblurred footage is strictly controlled, requiring multi-factor authentication for law enforcement and security personnel.

Real-Life Uses of Face Blur Technology

Face blur technology is being implemented in several key sectors:

Public Transportation Systems: Many modern train stations, subways, and airports have adopted face blur technology as part of their CCTV systems to protect the privacy of commuters. For instance, London's Heathrow Airport uses advanced video analytics with face blur to ensure footage meets GDPR compliance while enhancing security.

Retail Stores: Large retail chains, including Walmart, use face blur technology in their in-store cameras. This allows security teams to monitor activity and reduce theft while protecting the privacy of innocent customers.

Smart Cities: In Barcelona, Spain, a smart city initiative includes face blur technology to ensure privacy in public spaces while gathering data to improve city management and security. The smart cameras deployed in this project offer anonymized data to city officials, allowing them to monitor traffic, crowd control, and more without compromising individual identities.

Journalism and Humanitarian Work: Media organizations such as the BBC use face blurring technology in conflict zones or protests to protect the identities of vulnerable individuals. Additionally, NGOs employ similar technology in sensitive regions to prevent surveillance abuse by oppressive regimes.

Public Perception and Ethical Considerations

Public perception of surveillance technologies is a complex mix of support and concern. On one hand, people recognize the need for surveillance to enhance public safety, prevent crime, and even assist in emergencies. On the other hand, many are worried about mass surveillance, personal data privacy, and the potential for abuse by authorities or hackers.

By implementing face blur technology, institutions can address some of these concerns. Studies suggest that people are more comfortable with surveillance systems when privacy-preserving measures like face blur are in place. It demonstrates a commitment to privacy and reduces the likelihood of objections to the use of surveillance in public spaces (Zhang et al., 2021).

However, ethical challenges remain. The decision of when to unblur faces must be transparent and subject to clear guidelines, ensuring that this capability isn’t misused. In democratic societies, there is ongoing debate over how to strike a balance between security and privacy, and face blur technology offers a middle ground that respects individual rights while still maintaining public safety (Johnson & Singh, 2022).

Future of Face Blur Technology

As AI and machine learning continue to evolve, face blur technology will become more refined, offering enhanced accuracy in face detection and obfuscation. The future may also see advancements in customizing the level of blurring depending on context. For instance, higher levels of obfuscation could be applied in particularly sensitive areas, such as protests or political gatherings, to ensure that individuals' identities are protected (Chaudhary et al., 2023).

Face blur technology is also expected to integrate with broader privacy-enhancing technologies in surveillance systems, ensuring that even as surveillance expands, personal freedoms remain protected. Governments and businesses that embrace this technology are likely to be seen as leaders in ethical surveillance practices (Park et al., 2022).

Conclusion

The need for effective public surveillance is undeniable in today’s world, where security threats can arise at any time. However, the collection of facial images in public spaces raises significant privacy concerns. Face blur technology is a vital tool in addressing these issues, allowing for the balance between public safety and individual privacy. By leveraging AI, computer vision, and edge computing, face blur technology not only protects individual identities but also enhances public trust in surveillance systems.

References

Chaudhary, S., Patel, N., & Gupta, A. (2023). AI-enhanced privacy solutions for smart cities: Ethical considerations in urban surveillance. Journal of Smart City Innovation, 14(2), 99-112.

Johnson, M., & Singh, R. (2022). Ethical implications of face recognition in public spaces: Balancing privacy and security. Journal of Ethics and Technology, 18(1), 23-37.

Martinez, D., Loughlin, P., & Wei, X. (2022). Privacy-preserving techniques in public surveillance systems: A review. IEEE Transactions on Privacy and Data Security, 9(3), 154-171.

Nguyen, H., Wang, T., & Luo, J. (2023). Real-time face blurring for public surveillance: Challenges and innovations. International Journal of Surveillance Technology, 6(1), 78-89.

Park, S., Lee, H., & Kim, J. (2022). Privacy in smart cities: New technologies for anonymizing public surveillance data. Data Privacy Journal, 15(4), 45-61.

Tao, Z., Wang, Y., & Li, S. (2023). AI-driven face blurring in public surveillance: Technical challenges and future directions. Artificial Intelligence and Privacy, 8(2), 123-140.

Zhang, Y., Lee, S., & Roberts, J. (2021). Public attitudes toward surveillance technology and privacy protections. International Journal of Privacy and Data Protection, 7(4), 45-63.

[fatihulusoy]

Greetings folks! Do you remember our folks at Facebook now Meta? You know 'em, you love 'em! Yet some of them still not trust them.. Annd you folks right to do so.. Here is why:

İn recent weeks some of your pictures you took and post these platforms get flagged as "Made By AI" for no reason like this one..

An Instagram photo of the Kolkata Knight Riders, labeled incorrectly as "Made with AI". An Instagram photo of the Kolkata Knight Riders, labeled as “Made with AI.” Image Credit: Instagram (screenshot)

Well the thing is our folks at meta has been work on a new ai based picture identification project for rest of their platforms for the last few mounts called: PlatoNERF!

actual tech is pretty amazing actually Nerf (NEural Radiance Fields) tech is mostly used by mockups and quick cgi reference shots this is simply generates photo realistic and high resolution scans into 3d renders like photoscan but based of reflections of light.

But what is PlatoNERF? has design and made by both MIT and META's R&d labs have developed PlatoNeRF, an AI-powered computer vision technique that can use shadows to model 3D scenes, including objects blocked from view using both Nerf tech and Lidar scan.

its simply scans the pictures and can create 3d envoirmental model through it but how its decides if something is real or AI? Shadows!

if it cant detect any shadows it automatically flags the post!

i know im speaking like its a techno-horror story or some its a conspiracy theory but i swear this bug actually caused by this :)

So if we solve that case we can continue with our regular schedule see you guys tomorrow!

Sources:

for you guys to understand how NERF tech works: