#objectlabeling

[object-label]

Kusakabe Kimbei 日下部 金兵衛 (1841-1934) 

For our first object label post, I wanted to highlight a Meji-era photographer, Kusakabe Kimbei.

A photographer primarily based in Yokohama, Kusakabe Kimbei’s work was very popular with foreign visitors and as a result can be found in many notable collections outside of Japan. He received much of his photography training in Yokohama working as a colorist and assistant under the ateliers of Felice Beato (1832-1909) and Baron Raimund von Stillfried (1839-1911) during the 1860’s and 1870’s. His studio was active from 1881 to 1914, and at its height had two locations in Yokohama and one in Tokyo.

His work poetically captures 19th century Japan and reflects and builds on Beato’s style. The high painterly quality of his oeuvre is seen in the graceful composition of the photograph below, ethereally hand-tinted and printed on albumen paper.  

Image: Kusakabe Kimbei (日下部 金兵衛). 723. Autumn View of Maples, Oji, Tokio. c. 1895. Hand-tinted albumen print. Museum of Fine Arts, Boston.  

Like many photographers in Japan of the time, his photographic compositions often make references to the world of prints - seeing as his foreign clientele would be already familiar with the print works like those of Hokusai, Hiroshige, and Utamaro.

This photograph, 723. Autumn View of Maples, Oji, Tokio, in particular references the famous autumn maple leaves in Takinogawa. This particular view seems to highlight a view from Oji Inari Shrine.

Image: Totoya Hokkei (魚屋 北渓). Autumn Maples at the Takinogawa River, ca. 1833. Color woodblock print on paper, . Brooklyn Museum.

Image: Kobayashi Kiyochika (小林 清親). Takinogawa Köyö瀧の川紅葉, ca. 1880. Color woodblock print on paper, . Honolulu Museum of Art.

Further Reading:

Bennett, Terry. Early Japanese Images. Rutland: Tuttle, 1996. 

Wakita, Mio. Staging Desires: Japanese Femininity in Kusakabe Kimbei’s Nineteenth Century Souvenir Photography. Berlin: Reimer, Dietrich, 2013. 

Dobson, Sebastian, Anne Nishimura Morse, and Frederic Alan Sharf. Art and Artifice: Japanese  Photographs of the Meiji Era. Boston, MA: Museum of Fine Arts, 2004. 

Cabrejas Almena, M. Carmen. “Fotografía de ficción en Japón en el siglo XIX. Recreaciones de escenas  para el mercado Occidental”. Anales de Historia del Arte 19: pg. 257-270, 2009 

[wisepl]

Image Annotation For security Surveillance we create a dataset that will help your camera to recognize individuals, objects, animals, detect motion of humans.Object classification on security monitors through bounding box labelling. - Motion Detection - Face Detection #imageannotation #boundingbox #security #cameras #objectlabeling #objectdetection #facerecognition #faceannotation #machinelearning #computervision #artificialintelligence #wisepl #bigdata #datascience #securitysurveillance https://www.instagram.com/p/CRN7K-nDQea/?utm_medium=tumblr

[datacollectionforuni]

Tags Masterlist

Here you’ll find the main tags I used during my data collection. Note that this blog contains other memes, collected for a previous work and by personal interest. The memes collected for my Master’s thesis are all tagged #thesis data. The tags presented in the list below were used in order to organise the data collected for my thesis. This list is likely to be modified.

Types of source:

I decided to focus on only three types of visual “sources”.

#random/stock (random pictures and stock images)

#movie/anime screenshot (encompass movies, TV shows, Anime)

#video source (for example Vines and Youtube videos)

Type of meme:

These types often overlap with one another and are subject to change.

#objectlabelling

#subchange

#blank filling

#redraw

#decomposed

#reaction/explanation pic (this tag was abandoned quickly since the memes are almost always used in reaction)

#maybe (this tag is used for images or memes that might or might not fit in my work)

[wisepl]

Indoor Objects Detection with Bounding Boxes The use of bounding boxes is also very much high in detecting the indoor objects like furniture, tables, chairs, cupboards and electronic systems. For machines it helps to get an idea of a room and what kind of objects are placed there with their position and dimension making easier for ML model to detect such things easily in real-life scenario. Images annotated with bounding boxes deep learning technique helps to understand objects better. Wisepl annotation service includes bounding box annotation in cheap cost per object. It includes object labeling and classification. #imageannotation #boundingboxannotation #dataannotation #indoorobjects #objectlabeling #objectclassification #objectdetection #machinelearning #computervision #artificialintelligence #bigdata #datascience #wisepl #annotationservice #deeplearning #ai https://www.instagram.com/p/CLodLeDF_Bo/?igshid=fq2vwmwpeo8k

[wisepl]

Object Detection. Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations. Wisepl provide object detection service for machine learning and computer vision. #imageannotation #objectrecognition #objectdetection #objectlabeling #ADAS #dataannotation #datalabeling #boundingbox #annotationservice #machinelearning #computervision #ai #ml #datascience #deeplearning #bigdata #wisepl #artificialintelligenceai https://www.instagram.com/p/CLFKuaIlXxp/?igshid=wnegx25cekth

[wisepl]

Semantic Segmentation For ADAS Annotation. Semantic segmentation is used to label the objects appearing more than one. And each object labeled with unique color code without background noise. And ADAS annotation is done to eliminate such background noise. Wisepl Technologies offers a best-in-class semantic segmentation to annotate the different objects to understand the scene parsing in computer vision for ADAS annotation. #annotation #AdasAnnotation #boundingbox #semanticsegmentation #ADAS #wisepl #serviceprovider #machinelearning #computervision #dataannotation #datalabeling #drivermonitoring #ai #computervision #objectlabeling #deeplearning https://www.instagram.com/p/CK8IaWxD1HK/?igshid=sq1k34fj8r4w

[wisepl]

Aerial View of Human Tracking #aerialviewannotation #humantracking #objectlabeling #imageannotation #computervision #machinelearning #dataannotation #datalabeling #datascience #deeplearning #ai #ml #wiseplai https://www.instagram.com/p/CK1u37DFB_H/?igshid=1b0a49i0h2r69

[wisepl]

Object Detection in Indoor | Bounding Box Annotation

Wisepl helps to label your indoor object detection dataset collected from indoor scenes. Compared to other indoor datasets, We train deep learning-based object detectors with a number of state-of-the-art models and compare them in terms of speed and accuracy.

#householdannotation #indoorannotation #boundingbox #imageannotation #dataannotation #objectdetection #objectlabeling #machinelearning #deeplearning #ai #computervision #wisepl