Human activity recognition via temporal moment invariants

Abstract

Temporal invariant shape moments intuitively seem to provide an important visual cue to human activity recognition in video sequences. In this paper, an SVM based method for human activity recognition is introduced. With this method, the feature extraction is carried out based on a small number of computationally-cheap invariant shape moments. When tested on the popular KTH action dataset, the obtained results are promising and compare favorably with that reported in the literature. Furthermore our proposed method achieves real-time performance, and thus can provide latency guarantees to real-time applications and embedded systems.

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

“…For the automatic classification of pigmented skin lesions, there are numerous reliable classification methods [32][33][34][35][36] developed in literature with the aid of learning algorithms, such as Naïve Bayesian (NB), k-Nearest Neighbor (k-NN), Support Vector Machines (SVMs), Neural Networks (NNs), Conditional Random Fields (CRFs), etc. In this work, the current task of skin lesion detection is formulated as a typical binary classification problem, where there are two classes for skin lesions, and the ultimate goal is to assign an appropriate diagnostic class label (malignant melanoma or benign pigmented) to each skin lesion in dermatoscopic images.…”

“…There are numerous supervised learning algorithms [37][38][39] by which a skin lesion malignance detector can be trained. Due to its outstanding generalization capability and reputation of being a highly accurate paradigm, an SVM classier [40] is employed in the current detection framework.…”

A Discriminative Framework for Action Recognition Using f-HOL Features

“…In recent years, automatic recognition of human activities from both still images and video sequences has attracted tremendous research interest, due to its immense potential for many applications in various fields and domains [1,2]. Although many efficient applications are available for the purpose of human action recognition, the most active widespread application might be Human Computer Interaction (HCI), where no explicit user's actions (e.g., keystrokes and mouse clicks) are available to capture user input.…”

Generalized <i>α</i>-Entropy Based Medical Image Segmentation

“…Especially, algorithms for the delineation of anatomical structures and other regions of interest are a key component in assisting and automating specific radiological tasks. These algorithms, named image segmentation algorithms, play a fundamental role in many medical imaging applications such as the quantification of tissue volumes [3,4], diagnosis [5], localization of pathology [6,7], study of anatomical structure [8,9], treatment planning [10], partial volume correction of functional imaging data [11], and computer integrated surgery [12][13][14]. Techniques for carrying out segmentations vary broadly depending on some factors such as specific application, imaging modality, etc.…”

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