Analyzing the effect of imagery wavelength on the agreement between various image metrics and human detection performance of targets embedded in natural images

2001 ◽  
Vol 40 (9) ◽  
pp. 1877 ◽  
Author(s):  
Guy Aviram
Keyword(s):  
Detection Performance ◽  
Human Detection ◽  
Natural Images ◽  
Image Metrics

scholarly journals Human and monkey detection performance in natural images compared with V1 population responses

2015 ◽  
Vol 15 (12) ◽  
pp. 577
Author(s):  
Yoon Bai ◽  
Yuzhi Chen ◽  
Wilson Geisler ◽  
Eyal Seidemann
Keyword(s):  
Detection Performance ◽  
Natural Images ◽  
Population Responses

Human Detection Based on a New Gradient CENTRIST Feature

2014 ◽  
Vol 543-547 ◽  
pp. 2716-2719
Author(s):  
Tao Li ◽  
Tao Xiang ◽  
Ren Jie Huang ◽  
Xue Zhu Zhao
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Feature Vector ◽  
Detection Performance ◽  
Human Detection ◽  
Feature Descriptor ◽  
Image Patch ◽  
Method Base

This paper proposes a real-time and accurate human detection method base on a new Gradient CENTRIST feature descriptor. Firstly, the feature can characterizes not only local human appearance and shape but also implicitly represent the global contour. Secondly, it does not involve image pre-processing or feature vector normalization, and it only requires steps to test an image patch. Our main contribution is that a more reliable feature descriptor is found, which can get a better human detection. The experiments on the INRIA pedestrian dataset demonstrate that the detection performance is significantly improved.

Neuronal Population Decoding Explains the Change in Signal Detection Sensitivity Caused by Task-Irrelevant Perceptual Bias

2010 ◽  
Vol 22 (10) ◽  
pp. 2586-2614 ◽  
Author(s):  
Satohiro Tajima ◽  
Hiromasa Takemura ◽  
Ikuya Murakami ◽  
Masato Okada
Keyword(s):  
Motion Detection ◽  
Visual Processing ◽  
Detection Performance ◽  
Ideal Observer ◽  
Human Detection ◽  
Perceptual Bias ◽  
The Other ◽  
Detection Sensitivity ◽  
Neural Population ◽  
Task Irrelevant

Spatiotemporal context in sensory stimulus has profound effects on neural responses and perception, and it sometimes affects task difficulty. Recently reported experimental data suggest that human detection sensitivity to motion in a target stimulus can be enhanced by adding a slow surrounding motion in an orthogonal direction, even though the illusory motion component caused by the surround is not relevant to the task. It is not computationally clear how the task-irrelevant component of motion modulates the subject's sensitivity to motion detection. In this study, we investigated the effects of encoding biases on detection performance by modeling the stochastic neural population activities. We modeled two types of modulation on the population activity profiles caused by a contextual stimulus: one type is identical to the activity evoked by a physical change in the stimulus, and the other is expressed more simply in terms of response gain modulation. For both encoding schemes, the motion detection performance of the ideal observer is enhanced by a task-irrelevant, additive motion component, replicating the properties observed for real subjects. The success of these models suggests that human detection sensitivity can be characterized by a noisy neural encoding that limits the resolution of information transmission in the cortical visual processing pathway. On the other hand, analyses of the neuronal contributions to the task predict that the effective cell populations differ between the two encoding schemes, posing a question concerning the decoding schemes that the nervous system used during illusory states.

scholarly journals Human Detection via Image Denoising for 5G-Enabled Intelligent Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hui Li ◽  
Hang Zhou ◽  
Xiaoguo Liang ◽  
Fen Cai ◽  
Lingwei Xu ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Multiple Scales ◽  
Autonomous Driving ◽  
Detection Performance ◽  
Feature Representation ◽  
Human Detection ◽  
Intelligent Video Surveillance ◽  
Video Images ◽  
Detection Technology ◽  
Efficient Transmission

5G technology strongly supports the development of various intelligent applications, such as intelligent video surveillance and autonomous driving. And the human detection technology in intelligent video surveillance has also ushered in new challenges. A number of video images will be compressed for efficient transmission; the resulting incomplete feature representation of images will drop the human detection performance. Therefore, in this work, we propose a new human detection method based on compressed denoising. We exploit the quality factor in the compressed image and incorporate the pixel_shuffle inverse transform based on FFDNet to effectively improve the performance of image compression denoising, then HRNet and HRFPN are used to extract and fuse high-resolution features of denoised images, respectively, to obtain high-quality feature representation, and finally, a cascaded object detector is used for classification and bounding box regression to further improve object detection performance. At last, the experimental results on PASCAL VOC show that the proposed method effectively removes the compression noise and further detects human objects with multiple scales and different postures. Compared with the state-of-the-art methods, our method achieved better detection performance and is, therefore, more suited for human detection tasks.

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