Journal ArticleDOI
GHT-based associative memory learning and its application to Human action detection and classification
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TLDR
This paper presents a novel approach to learning an associative memory model using Generalized Hough Transform (GHT) and shows that the proposed method gives good performance on several publicly available datasets in terms of detection accuracy and recognition rate.About:
This article is published in Pattern Recognition.The article was published on 2013-11-01. It has received 10 citations till now. The article focuses on the topics: Content-addressable memory.read more
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Book ChapterDOI
Action Recognition in Realistic Sports Videos
Khurram Soomro,Amir Roshan Zamir +1 more
TL;DR: This chapter provides a detailed study of the prominent methods devised for action localization and recognition in videos and argues that performing the recognition on temporally untrimmed videos and attempting to describe an action, instead of conducting a forced-choice classification, are essential for analyzing the human actions in a realistic environment.
Journal ArticleDOI
Image based computer aided diagnosis system for cancer detection
Howard Lee,Yi-Ping Phoebe Chen +1 more
TL;DR: Computer vision techniques adopted in medical image analysis, in particular, for cancer detection, focused on the most common form of cancer types, namely breast cancer, prostate cancer, lung cancer and skin cancer are reviewed.
Journal ArticleDOI
Discriminative binary feature learning and quantization in biometric key generation
Amir Anees,Yi-Ping Phoebe Chen +1 more
TL;DR: An efficient unified framework for generating stable, robust and secure cryptography keys based on facial features, without the need to save information related to facial features in the database is developed.
Journal ArticleDOI
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
TL;DR: Wasserstein quantified transferability framework as mentioned in this paper was developed to highlight wide-range transferable contextual dependencies, and a self-supervised pseudo label generator was designed to equally provide confident pseudo pixel labels for both hard to transfer and easy to transfer target samples, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent negative transfer of untransferable representations.
Posted Content
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
TL;DR: A new weakly-supervised lesions transfer framework is proposed, which can not only explore transferable domain-invariant knowledge across different datasets, but also prevent the negative transfer of untransferable representations.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
Object Detection with Discriminatively Trained Part-Based Models
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Journal ArticleDOI
An efficient k-means clustering algorithm: analysis and implementation
Tapas Kanungo,David M. Mount,Nathan S. Netanyahu,Christine D. Piatko,Ruth Silverman,Angela Y. Wu +5 more
TL;DR: This work presents a simple and efficient implementation of Lloyd's k-means clustering algorithm, which it calls the filtering algorithm, and establishes the practical efficiency of the algorithm's running time.
Journal ArticleDOI
Generalizing the hough transform to detect arbitrary shapes
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
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