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The eye structure of insects, which is called a compound eye, has interesting advantages. It has a large field of view, low aberrations, compact size, short image processing time, and an infinite depth of field. If we can design a compound eye camera which mimics the compound eye structure of insects, compound images with these interesting advantages can be obtained. In this paper, we consider the...
In bridge buildings, concrete is widely used because its materials are considerably low-cost and it has high plasticity. However, some drawbacks exist in this kind of bridges, and crack is the most common ones. In order to avoid the cracks in bridge buildings becoming worse, it is necessary to periodically perform the inspection for it. Thus, a bridge inspection robot system with machine vision is...
This paper introduces a new method for multiobject segmentation in images, named as Hierarchical Layered Oriented Image Foresting Transform (HLOIFT). As input, we have an image, a tree of relations between image objects, with the individual high-level priors of each object coded in its nodes, and the objects' seeds. Each node of the tree defines a weighted digraph, named as layer. The layers are then...
Potato as the fourth largest staple food in China, The external defect detection directly affects the industrialization of potato and deep processing. As the currently domestic testing method are mostly based on specific circumstances, specific light, which does not satisfy the testing requirements of actual environment. Therefore, this paper presents a non-destructive method for the study of green,...
This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective...
In this paper, we show that topological persistence can be employed in biomedical image processing to perform object segmentation. First we model the pixels of the image by combinatorial transformation into a cubical complex that we will call the pixels' complex. Then a nested sequence of complexes is built on which the persistent homology is computed. By identifying the 1D chains with large life...
An algorithm for detecting and extracting crack defects in glassware using wavelet transform is proposed in this paper. Firstly, the canny image segmentation and the local adaptive dynamic threshold segmentation are carried out on the glassware image with unobvious crack defects. Then, the wavelet decomposition is applied separately on the segmented images. And finally the wavelet fusion is used to...
A first-person camera, placed at a person's head, captures, which objects are important to the camera wearer. Most prior methods for this task learn to detect such important objects from the manually labeled first-person data in a supervised fashion. However, important objects are strongly related to the camera wearer's internal state such as his intentions and attention, and thus, only the person...
This paper proposes a novel approach for segmenting primary video objects by using Complementary Convolutional Neural Networks (CCNN) and neighborhood reversible flow. The proposed approach first pre-trains CCNN on massive images with manually annotated salient objects in an end-to-end manner, and the trained CCNN has two separate branches that simultaneously handle two complementary tasks, i.e.,...
The quality control is a very important task in industrial systems. When the quality control of a product has been made during production, the manufacturing defects will be minimized. For this purpose, automatic inspection system has been developed. In his study, a new vision based method is proposed for quality control and inspection purposes. The proposed method uses interactive segmentation which...
Moving vehicle segmentation in traffic videos is a challenging work because of complex background and variety objects. In this paper, we focus on detecting vehicles that are running through crossroads using the up-to-date spatiotemporal saliency model. The current saliency detection methods aim at detecting the most salient objects, novel but stationary target will be easily classified as foreground,...
Segmentation process in an essential part in image processing to obtain good preparation either for further process of data mining or object recognition. This paper proposes a new method of segmenting tomato image for clustering its ripeness. The tomato images are taken from three types of smartphone camera in various lighting condition with white background. When taking picture by using smartphone...
Traditional image segmentation methods working with low level image features are usually difficult to adapt to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research field. It aims at obtaining more meaningful segments related to semantic objects in the scene by analyzing a combination of different information. 3D point...
Most pedestrian detection algorithms only provide the object region instead of the actual body segmentation in video. For reducing the large number of redundant information and extracting a clear contour and texture feature of an up-right person, a superpixel segmentation algorithm with region correlation saliency analysis is proposed from coarse to fine cutting without any prior information. This...
We propose an end-to-end learning framework for segmenting generic objects in videos. Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos. We formulate this task as a structured prediction problem and design a two-stream fully convolutional neural network which fuses together motion and appearance in a unified...
We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation [29] and instance mask proposal [5]. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest...
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce the concept of convnet-based guidance applied to video object segmentation. Our model proceeds on a per-frame basis, guided by the output of the previous frame towards the object of interest in the next frame. We demonstrate that highly accurate object segmentation in videos can be enabled by using...
A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, is proposed in this work. We propagate the segmentation labels at the previous frame to the current frame using optical flow vectors. However, the propagation is error-prone. Therefore, we develop the convolutional trident network (CTN), which has three decoding branches:...
Object segmentation in weakly labelled videos is an interesting yet challenging task, which aims at learning to perform category-specific video object segmentation by only using video-level tags. Existing works in this research area might still have some limitations, e.g., lack of effective DNN-based learning frameworks, under-exploring the context information, and requiring to leverage the unstable...
Recently, researchers have made great processes to build category-specific 3D shape models from 2D images with manual annotations consisting of class labels, keypoints, and ground truth figure-ground segmentations. However, the annotation of figure-ground segmentations is still labor-intensive and time-consuming. To further alleviate the burden of providing such manual annotations, we make the earliest...
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