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Sang Hack Jung, Ajay Divakaran, Harpreet Singh Sawhney: Method for pose invariant vessel fingerprinting. SRI International, Moser Taboada, December 11, 2012: US08330819

A computer-implemented method for for matching objects is disclosed. At least two images where one of the at least two images has a first target object and a second of the at least two images has a second target object are received. At least one first patch from the first target object and at least ...


62
Ajay Divakaran, Kadir A Peker: Video mining using unsupervised clustering of video content. Mitsubishi Electric Research Laboratories, May 6, 2004: US20040085323-A1

A method mines unknown content of a video by first selecting one or more low-level features of the video. For each selected feature, or combination of features, time series data is generated. The time series data is then self-correlated to identify similar segments of the video according to the low- ...


63
Ajay Divakaran, Kadir A Peker: Blind summarization of video content. Mitsubishi Electric Research Laboratories, May 6, 2004: US20040085339-A1

A method summarizes unknown content of a video. First, low-level features of the video are selected. The video is then partitioned into segments according to the low-level features. The segments are grouped into disjoint clusters where each cluster contains similar segments. The clusters are labeled ...


64
Ajay Divakaran: Pattern discovery in video content using association rules on multiple sets of labels. Mitsubishi Electric Research Laboratories, May 6, 2004: US20040086180-A1

A method discovers patterns in unknown content of a video. The video is partitioned into sets of disjoint segments. Each set includes all frames of the video, and each set is partitioned according to a selected low-level feature of the video. The disjoint segments are grouped into corresponding sets ...


65
Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran: Method and system for extracting sports highlights from audio signals. Mitsubishi Electric Research Laboratories, August 26, 2004: US20040167767-A1

A method extracts highlights from an audio signal of a sporting event. The audio signal can be part of a sports videos. First, sets of features are extracted from the audio signal. The sets of features are classified according to the following classes: applause, cheering, ball hit, music, speech and ...


66
Ajay Divakaran, Regunathan Radhakrishnan: Audio-Assisted segmentation and browsing of news videos. Mitsubishi Electric Research Laboratories, July 22, 2004: US20040143434-A1

A method segments and summarizes a news video using both audio and visual features extracted from the video. The summaries can be used to quickly browse the video to locate topics of interest. A generalized sound recognition hidden Markov model (HMM) framework for joint segmentation and classificati ...


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Ajay Divakaran, Ziyou Xiong, Regunathan Radhakrishnan, Kadir A Peker, Koji Miyahara: Method for detecting short term unusual events in videos. Mitsubishi Electric Research Laboratories, December 30, 2004: US20040268380-A1

A method detects short term, unusual events in a video. First, features are extracted features from the audio and the video portions of the video. Segments of the video are labeled according to the features. A global sliding window is applied to the labeled segments to determine global characteristi ...


68
Kadir A Peker, Ajay Divakaran: Visual complexity measure for playing videos adaptively. Patent Department, Mitsubishi Electric Research Laboratories, January 27, 2005: US20050018881-A1

A method plays frames of a video adaptively according to a visual complexity of the video. First a spatial frequency of pixel within frames of the video is measured, as well as a temporal velocity of corresponding pixels between frames of the video. The spatial frequency is multiplied by the tempora ...


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Kadir A Peker, Ajay Divakaran: Method and system for segmenting videos using face detection. Mitsubishi Electric Research Laboratories, April 26, 2007: US20070091203-A1

A method generates a summary of a video. Faces are detected in a plurality of frames of the video. The frames are classified according to a number of faces detected in each frame and the video is partitioned into segments according to the classifications to produce a summary of the video. For each f ...


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Regunathan Radhakrishnan, Ajay Divakaran: Dynamic generative process modeling, tracking and analyzing. Mitsubishi Electric Research Laboratories, January 11, 2007: US20070010998-A1

A method tracks and analyzes dynamically a generative process that generates multivariate time series data. In one application, the method is used to detect boundaries in broadcast programs, for example, a sports broadcast and a news broadcast. In another application, significant events are detected ...