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Since news videos are valuable sources of multimedia information on real-world events, there is a demand for viewing them efficiently. However, there is a problem that summarization methods based on auditory contents do not take into account the visual contents. In the case of news videos, due to its presentation style where audio contents and visual contents do not necessarily come from the same...
Automated image stylization to create artistically pleasing images from ordinary photographs is an interesting and useful task in computer vision. Therefore, several automated styling methods have been developed using powerful Deep Neural Network (DNN) features. They typically use a carefully constructed joint loss function to separately consider the similarities between a proposed output and the...
This paper presents a new line based 6-DOF monocular algorithm that uses the iSAM2, a point-based Graph SLAM approach. We extend iSAM2 to minimize the reprojection error of the line features to solve the line-based SLAM problem. A specific line representation is exploited that combines the Plücker Coordinates and the Cayley representation. The Plücker Coordinates are used for the 3D line projection...
Underwater image segmentation becomes a difficult and challenging task due to various perturbations present in the water. In this paper we propose a novel method for underwater image segmentation based on M-band wavelet transform and human psychovisual phenomenon(HVS). The M-band wavelet transform captures the texture of the underwater image by decomposing the image into sub bands with different scales...
Karyotyping helps to evaluate the size, shape and number of chromosomes. It is a screening and diagnostic process for finding various abnormalities related with chromosomes. Banding pattern is unique for each pair of chromosome. In the present research, fractional derivatives find an important place in the field of signal processing and digital image processing. This paper proposes a method for segmenting...
We report on the results of the first visual search and rating study (N60) evaluating human gaze when assessing the realism of image composites. The effects of object identity knowledge and mismatched feature type on observers' gaze and subjective realism scores are studied. Gaze metrics used include: fixation count, fixation duration, time and duration of first fixation on target object, as well...
This paper is devoted to investigation of features that will be the most appropriate for description of high resolution satellite imagery. We developed an image description model which is based on the distribution of image object classes. Proposed model could be used for image similarity estimation.
As the human eye on the image of different regions of the contrast sensitivity is different, it is particularly important to segment the image region more accurately in the image quality evaluation. Based on this, this paper presents a non-reference image region division method based on deep learning. Firstly, the Canny operator performs image edge detection at low threshold to obtain the strong edge...
The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes. Annotation is performed in a dense and fine-grained style by using polygons for delineating individual objects. Our dataset is 5× larger than the total amount of fine annotations for Cityscapes...
Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-levelfine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such...
In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first modeling images and sentences independently and then segment images by combining these two types of representations. We argue that learning word-to-image interaction is more native in the sense of jointly modeling two modalities...
Loop closure detection is an important part of visual simultaneous location and mapping (SLAM) system. Most of traditional loop closure detection approaches using hand-crafted features often lack robustness with respect to object occlusions and illumination changes, especially for the complicated indoor environment. Recently, convolutional neural network (CNN) makes a huge impact on many computer...
Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs a significant number of pixel-level annotated data, which is often unavailable. To address this lack of annotations, in this paper, we leverage, on one hand, a massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images...
Fully convolutional network (FCN) has been successfully applied in semantic segmentation of scenes represented with RGB images. Images augmented with depth channel provide more understanding of the geometric information of the scene in the image. The question is how to best exploit this additional information to improve the segmentation performance.,,In this paper, we present a neural network with...
The rapid and irregular motion of semen cells makes the counting process of semen difficult in the visual assessment. Therefore, computer based techniques are necessary to evaluate the tests with more accurately. In this paper, an alternative way to the visual assessment technique in spermiogram tests is presented. Analyses are performed on the recorded microscope video images by computer, automatically...
The advent of high-tech journaling tools facilitates an image to be manipulated in a way that can easily evade state-of-the-art image tampering detection approaches. The recent success of the deep learning approaches in different recognition tasks inspires us to develop a high confidence detection framework which can localize manipulated regions in an image. Unlike semantic object segmentation where...
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...
Rich and dense human labeled datasets are among the main enabling factors for the recent advance on visionlanguage understanding. Many seemingly distant annotations (e.g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes — and even the same set of images (e...
Unsupervised learning from visual data is one of the most difficult challenges in computer vision. It is essential for understanding how visual recognition works. Learning from unsupervised input has an immense practical value, as huge quantities of unlabeled videos can be collected at low cost. Here we address the task of unsupervised learning to detect and segment foreground objects in single images...
The quality control of cherries harvested in the orchard is a process of great relevance for the Chilean export industry. Nowadays companies carry out this process manually, obtaining a high error rate in the measurements of color and caliber of the fruits. This article seeks to develop a system to automate this process and thus reduce measurement failures. For this, an information system was implemented...
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