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Sentiment prediction from visual content is a challenge due to the difficulty of inferring sentiment directly from the low-level visual features. Most recent researches use Adjective Noun Pairs (ANPs) as a middle level to narrow the gap between vision and sentiment. While as Convolutional Neural Networks (CNNs) is going deeper, it is becoming possible to implement rather complex mappings. In this...
Nowadays, people might need super resolution to obtain high quality images. Super resolution algorithm enhances high frequency information (texture or edges) to improve the image quality. We can do more things with super resolution, such as road surveillance system. The image quality would be degraded by illumination, angle, distance, and other conditions, and it will result in failing to recognize...
With the development of image processing and computer vision technology, using gesture to communicate with the machine will not only appear in scientific move or just a conceptual product. Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. With this, we can have a more convenient life. Therefore, our...
In this paper we investigate the methods for image keypoint detection, description, and matching. We compare four methods, including SIFT, SURF, ORB, and BRISK. The datasets used for evaluation cover different changes and transformations in imaging conditions, such as zoom, rotation, blur, viewpoint, light, and JPEG compression. We also compare the computational speed of these four methods.
The recent attentions on video analysis have led to the extensive enhancement of computer vision technologies. However, it is still necessary to closely monitor the results of video analysis by human operators. This paper introduces a trajectory-based scene retrieval system that can help human operators look closely at desired scenes based on a trajectory query. The system includes the index and mapper...
The exercise and rehabilitation based on computer vision system have many benefits and it has attracted the interest of researchers in the computer vision community. The researchers have kept on improving the existing methods by creating novel method or developing new algorithms in image processing and artificial intelligence. This paper presents the experiment and analysis of wrist radial and ulnar...
This paper proposes a Bag of Visual Words (BoVW) based approach for keyword spotting on the Mongolian historical document images. In this paper, the first step is dividing the scanned Mongolian historical document images into word images by some preprocessing steps, such as connected component analysis, binarization etc. Then, all of image in our training set are processed in the following steps,...
In this document, an adaptive algorithm is proposed to identify anomalies in moving objects, such as pedestrians, cars, motorcyclists and cyclists. The anomalies detected by this algorithm are: occlusions, an object partially entering or exiting a frame, alterations in the object velocities or collisions between two or more objects in the frame. The classification between frames with anomaly and no...
As the technology for acquiring and storing images becomes more prevalent, we are faced with a growing need to sort and label these images. At this time, computer vision algorithms cannot parse abstract concepts from images like a human. As a result, there may be performance gains possible from the integration of human analysts with computer vision agents. We present an image triage system which facilitates...
Hand shape classification is an important problem for the human computer interaction and the finger-spelling recognition. For this matter, what is needed is a real-time processing and scale invariance. To this end, we propose a feature vector for hand shape classification, which is fast, and robust to scale. The proposed method calculates an adaptive k-curvature which computes a curvature depending...
Haze is mainly occurred by atmospheric phenomena. Recently, many researchers in haze removal algorithm area are using single image. At the single image, we can't use depth information. To estimate the thickness of haze without depth information is not easy. As a result, single image haze removal method includes halo effect. In this paper, we propose halo effect suppression for single image haze removal...
We propose the DeepPose-based pose estimation system that is flexible with the change of bounding-box range for top-view images. Our purpose is to link person detection system and pose estimation system. We introduce Bounding-box Curriculum Learning (BCL) and Recurrent Pose Estimation (RPE). BCL is a learning technique of CNN inspired from Curriculum Learning. RPE is a recurrent process of pose estimation...
In this paper, we presented a comparison between different approaches of person re-identification in camera network based on the-state-of-the-art. We studied the different descriptors of objects for identifying people and existing classifier at the re-identification step. We seek to develop video surveillance systems online in controlled areas and improve their reliability and their processing time...
The present paper describes a low-cost algorithm for video stabilization. Like other feature based algorithms, it is robust to motion blur, noise and illumination changes. Moreover, maintaining real time processing, it is not negatively affected by moving objects in the scene, works fine even in conditions of low details in the background and it is robust to scene changes.
Current research in computer vision and machine learning has demonstrated some great abilities at detecting and recognizing objects in natural images. The promising results in these areas have inspired research towards solving more complex multi-modal learning problems in the image/video domains such as automatic annotation, segmentation, labelling, and generic understanding. Although solutions have...
Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
In this paper, we propose a new facial landmarks detection method based on deep learning with facial contour and facial components constraints. The proposed deep convolutional neural networks (DCNNs) for facial landmark detection consists of two deep networks: one DCNN is to detect landmarks constrained on the facial contour and the other is to detect landmarks constrained on facial components. A...
In this paper, we propose a novel framework for nighttime image dehazing based on a nighttime haze model which accounts for varying light sources and their glow. First, glow effects are decomposed using relative smoothness. Atmospheric light is then estimated by combining global and local atmospheric lights using a local atmospheric selection map. The transmission is estimated by maximizing an objective...
Computing matching cost by Convolutional neural networks(CNNs) work well in fetching accurate dense disparity maps. But these methods still have problems: (1) they always employ equal weights for left and right images in convolutional layers, losing relational information of patches; (2) they don't solve the balance between patches' size and processing efficiency, the larger size the more information...
Obstacle detection is one of the key requirements for autonomous vehicles. Many researchers have developed the techniques for obstacle detection for the safety of the drivers. Vision-based technique is one of popular obstacle detection techniques. We survey vision-based obstacle detection approaches, and present two approaches. In future work, we intend to implement a dynamic obstacle detection technique...
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