Parameter Study and Evaluation of Stepped Multiple Frequency Radar for Centimeter-Class Object Detection in Background Clutter Environments

2022 ◽  
Vol 142 (1) ◽  
pp. 58-66
Author(s):  
Takayuki Inaba ◽  
Manabu Akita
Keyword(s):  
Object Detection ◽  
Parameter Study ◽  
Multiple Frequency ◽  
Background Clutter

scholarly journals Fast object detection in digital grayscale images

2009 ◽  
Vol 63 (3) ◽  
pp. 116-124 ◽  
Author(s):  
Aivars Lorencs ◽  
Ints Mednieks ◽  
Juris Siņica-Siņavskis
Keyword(s):  
Object Detection ◽  
A Priori ◽  
A Priori Information ◽  
Specific Object ◽  
Potential Applications ◽  
Background Clutter ◽  
Priori Information ◽  
Time Systems ◽  
Application Specific ◽  
Grayscale Images

Fast object detection in digital grayscale images The problem of specific object detection in digital grayscale images is considered under the following conditions: relatively small image fragments can be analysed (a priori information about the size of objects is available); images contain a varying undefined background (clutter) of larger objects; processing time should be minimised and must be independent from the image contents; proposed methods should provide for efficient implementation in application-specific electronic circuits. The last two conditions reflect the aim to propose approaches suitable for application in real time systems where known sophisticated methods would be inapplicable. The research is motivated by potential applications in the food industry (detection of contaminants in products from their X-ray images), medicine (detection of anomalies in fragments of computer tomography images etc.). Possible objects to be detected may include compact small objects, curved lines in different directions, and small regions of pixels with brightness different from the background. The paper describes proposed image processing approaches to detection of such objects and the results obtained from processing of sample food images.

scholarly journals A New Edge Patch with Rotation Invariance for Object Detection and Pose Estimation

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 887 ◽  
Author(s):  
Xunwei Tong ◽  
Ruifeng Li ◽  
Lianzheng Ge ◽  
Lijun Zhao ◽  
Ke Wang
Keyword(s):  
Object Detection ◽  
Pose Estimation ◽  
Rotation Invariance ◽  
Depth Image ◽  
Detection Accuracy ◽  
Edge Pixel ◽  
Local Patch ◽  
Background Clutter ◽  
Public Datasets ◽  
Local Sampling

Local patch-based methods of object detection and pose estimation are promising. However, to the best of the authors’ knowledge, traditional red-green-blue and depth (RGB-D) patches contain scene interference (foreground occlusion and background clutter) and have little rotation invariance. To solve these problems, a new edge patch is proposed and experimented with in this study. The edge patch is a local sampling RGB-D patch centered at the edge pixel of the depth image. According to the normal direction of the depth edge, the edge patch is sampled along a canonical orientation, making it rotation invariant. Through a process of depth detection, scene interference is eliminated from the edge patch, which improves the robustness. The framework of the edge patch-based method is described, and the method was evaluated on three public datasets. Compared with existing methods, the proposed method achieved a higher average F1-score (0.956) on the Tejani dataset and a better average detection rate (62%) on the Occlusion dataset, even in situations of serious scene interference. These results showed that the proposed method has higher detection accuracy and stronger robustness.

Content-Based Image Retrieval

2011 ◽  
pp. 115-144 ◽  
Author(s):  
Ming Zhang ◽  
Reda Alhajj
Keyword(s):  
Image Retrieval ◽  
Object Detection ◽  
Image Representation ◽  
Point Of View ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
Image Descriptors ◽  
Key Issues ◽  
Feature Based ◽  
Background Clutter

Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without the help of annotations. The current CBIR systems use global features (e.g., color, texture, and shape) as image descriptors, or usefeatures extracted from segmented regions (called region-based descriptors). In the former case, descriptors are not discriminative enough at the object level and are sensitive to object occlusion or background clutter, thus fail to give satisfactory result. In the latter case, the features are sensitive to the image segmentation, which is a difficult task in its own right. In addition, the region-based descriptors are still not invariant to varying imaging conditions. In this chapter, we look at the CBIR from the object detection/recognition point of view and introduce the local feature-based image representation methods recently developed in object detection/recognition area. These local descriptors are highly distinctive and robust to imaging condition change. In addition to image representation, we also introduce the other two key issues of CBIR: similarity measurement for image descriptor comparison and the index structure for similarity search.

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%.

Object Detection in Cluttered Range Images Using Edgel Geometry

2010 ◽  
Vol 130 (9) ◽  
pp. 1572-1580
Author(s):  
Dipankar Das ◽  
Yoshinori Kobayashi ◽  
Yoshinori Kuno
Keyword(s):  
Object Detection ◽  
Range Images
Sign in / Sign up

Export Citation Format

Share Document