Fast object detection using boosted co-occurrence histograms of oriented gradients

2010 ◽  
Author(s):  
Haoyu Ren ◽  
Cher-Keng Heng ◽  
Wei Zheng ◽  
Luhong Liang ◽  
Xilin Chen
Keyword(s):  
Object Detection ◽  
Histograms Of Oriented Gradients

Double Feature Combination: Region Contrast for Visual Salient Object Detection

2012 ◽  
Vol 239-240 ◽  
pp. 811-815
Author(s):  
Zhi Hai Sun ◽  
Teng Song ◽  
Wen Hui Zhou ◽  
Hua Zhang
Keyword(s):  
Object Detection ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Salient Object Detection ◽  
Color Contrast ◽  
Salient Object ◽  
Histograms Of Oriented Gradients ◽  
Visual Saliency Detection

Visual saliency detection has become an important step between computer vision and digital image processing. Recent methods almost form a computational model based on color, which are difficult to overcome the shortcoming with cluttered and textured background. This paper proposes a novel salient object detection algorithm integrating with region color contrast and histograms of oriented gradients (HoG). Extensively experiments show that our algorithm outperforms other state-of-art saliency methods, yielding higher precision and better recall rate, even lower mean absolution error.

Detecting Human Actions by 3D Deformable Parts Models

2014 ◽  
Vol 981 ◽  
pp. 331-334
Author(s):  
Ming Yang ◽  
Yong Yang
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
High Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Actions ◽  
Still Images ◽  
Histograms Of Oriented Gradients ◽  
Deformable Part Models ◽  
Unified Method

In this paper, we introduce the high performance Deformable part models from object detection into human action recognition and localization and propose a unified method to detect action in video sequences. The Deformable part models have attracted intensive attention in the field of object detection. We generalize the approach from 2D still images to 3D spatiotemporal volumes. The human actions are described by 3D histograms of oriented gradients based features. Different poses are presented by mixture of models on different resolutions. The model autonomously selects the most discriminative 3D parts and learns their anchor positions related to the root. Empirical results on several video datasets prove the efficacy of our proposed method on both action recognition and localization.

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

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