Detecting Objects with High Object Region Percentage

2021 ◽  
Author(s):  
Fen Fang ◽  
Qianli Xu ◽  
Liyuan Li ◽  
Ying Gu ◽  
Joo-Hwee Lim
Keyword(s):  
Object Region

scholarly journals Segmentation of Database Images Using Gradient Based Multiscale Morphological Reconstruction

2015 ◽  
pp. 271-278
Author(s):  
A. Nithya ◽  
R. Kayalvizhi
Keyword(s):  
Large Scale ◽  
Object Segmentation ◽  
Object Identification ◽  
Segmentation Algorithm ◽  
Phase Object ◽  
Morphological Reconstruction ◽  
Object Based ◽  
Gradient Based ◽  
Object Region ◽  
Identification Phase

The main purpose of this research is to improve the accuracy of object segmentation in database images by constructing an object segmentation algorithm. Image segmentation is a crucial step in the field of image processing and pattern recognition. Segmentation allows the identification of structures in an image which can be utilized for further processing. Both region-based and object-based segmentation are utilized for large-scale database images in a robust and principled manner. Gradient based MultiScalE Graylevel mOrphological recoNstructions (G-SEGON) is used for segmenting an image. SEGON roughly identifies the background and object regions in the image. This proposed method comprises of four phases namely pre-processing phase, object identification phase, object region segmentation phase, majority selection and refinement phase. After developing the grey level mesh the resultant image is converted into gradient and K-means clustering segmentation algorithm is used to segment the object from the gradient image. After implementation the accuracy of the proposed G-SEGON technique is compared with the existing method to prove its efficiency.

scholarly journals Content-based Image Retrieval using Color Ratio and Moment of Object Region

2002 ◽  
Vol 9B (4) ◽  
pp. 501-508
Author(s):  
Eun-Kyong Kim ◽  
Jun-Taek Oh ◽  
Wook-Hyun Kim
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Color Ratio ◽  
Object Region

scholarly journals Object-Independent Grasping in Heavy Clutter

2020 ◽  
Vol 10 (3) ◽  
pp. 804 ◽  
Author(s):  
HyunJun Jo ◽  
Jae-Bok Song
Keyword(s):  
Spatial Clustering ◽  
Computational Time ◽  
Success Rates ◽  
Computational Costs ◽  
Difference Image ◽  
Cluttered Environment ◽  
Depth Difference ◽  
Dbscan Algorithm ◽  
Bin Picking ◽  
Object Region

When grasping objects in a cluttered environment, a key challenge is to find appropriate poses to grasp effectively. Accordingly, several grasping algorithms based on artificial neural networks have been developed recently. However, these methods require large amounts of data for learning and high computational costs. Therefore, we propose a depth difference image-based bin-picking (DBP) algorithm that does not use a neural network. DBP predicts the grasp pose from the object and its surroundings, which are obtained through depth filtering and clustering. The object region is estimated by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, and a depth difference image (DDI) that represents the depth difference between adjacent areas is defined. To validate the performance of the DBP scheme, bin-picking experiments were conducted on 45 different objects, along with bin-picking experiments in heavy clutters. DBP exhibited success rates of 78.6% and 83.3%, respectively. In addition, DBP required a computational time of approximately 1.4 s for each attempt.

scholarly journals A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 946 ◽  
Author(s):  
Wenzhao Feng ◽  
Chunhe Hu ◽  
Yuan Wang ◽  
Junguo Zhang ◽  
Hao Yan
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Transmission Efficiency ◽  
Signal Interference ◽  
Progressive Transmission ◽  
Wireless Multimedia ◽  
Transmission Method ◽  
Hierarchical Coding ◽  
Object Region

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.

scholarly journals Object Detection Based on Region Decomposition and Assembly

2019 ◽  
Vol 33 ◽  
pp. 8094-8101 ◽  
Author(s):  
Seung-Hwan Bae
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Performance Improvement ◽  
Semantic Relations ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Multi Scale ◽  
Object Proposals ◽  
Object Region ◽  
High Level

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided the promising detection results. However, the detection accuracy is degraded often because of the low discriminability of object CNN features caused by occlusions and inaccurate region proposals. In this paper, we therefore propose a region decomposition and assembly detector (R-DAD) for more accurate object detection.In the proposed R-DAD, we first decompose an object region into multiple small regions. To capture an entire appearance and part details of the object jointly, we extract CNN features within the whole object region and decomposed regions. We then learn the semantic relations between the object and its parts by combining the multi-region features stage by stage with region assembly blocks, and use the combined and high-level semantic features for the object classification and localization. In addition, for more accurate region proposals, we propose a multi-scale proposal layer that can generate object proposals of various scales. We integrate the R-DAD into several feature extractors, and prove the distinct performance improvement on PASCAL07/12 and MSCOCO18 compared to the recent convolutional detectors.

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