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A single RGB image based 3D object reconstruction system 15

A single RGB image based 3D object reconstruction system

引用

International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021

作者： Oriti, Damiano Sanna, Andrea de Pace, Francesco Manuri, Federico Tamburello, Francesco Ronzino, Fabrizio Dipartimento di Automatica e Informatica Politecnico di Torino Corso Duca degli Abruzzi 24 TorinoI-10129 Italy

ISBN: (纸本)9789898704320

Easy and fast digitalization of real objects is especially useful when considering augmented reality (AR) and virtual reality (VR), as reconstructed objects allow a better interaction between the real and virtual worlds than using pre-made 3D CAD models. thanks to the ubiquity of smartphones and to the spread of immersive VR devices, the AR and VR technologies are rapidly becoming popular. However, an affordable, robust and easy to use solution for object digitalization is still missing. this paper presents a reconstruction system that allows users to convert a single photo of a real object into a digital 3D asset. A smartphone is used to capture a snapshot of the object, whereas a secondary computing device performs the reconstruction process by exploiting a pipeline of three Deep Learning methods. Several experiments have been conducted in order to assess the accuracy and robustness of the system by using a standard metric for measuring the reconstruction accuracy (chamfer distance). the main outcomes show that the proposed system has a comparable accuracy with respect to the state-of-the-art methods for 3D object reconstruction. © 2021 International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Augmented reality

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Integrated Algorithm for Different Tracking Challenges 5

Integrated Algorithm for Different Tracking Challenges

引用

5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Shah, Jay Sai Subrahmanyam, G. R. K. Mishra, Deepak Indian Inst Space Sci & Technol Dept Avion Trivandrum 695547 Kerala India Indian Inst Space Sci & Technol Dept Earth & Space Sci Trivandrum 695547 Kerala India

ISBN: (纸本)9781467385640

this paper presents an efficient combination of two well-known tracking algorithms, Tracking-Learning-Detection (TLD) and Compressive Tracking (CT) to devise an algorithm which takes advantages of both and outperforms them on their short-ends by virtue of other. TLD fails in cases including full out-of-plane rotation, fast motion and articulated object tracking. While CT fails in resuming tracking once the object leaves the frame and comes back. We propose a combining algorithm mentioned as Algorithm 1, which robustly handles all the tracking challenges. Different thresholds are set which can be varied to weigh each component as required. the proposed algorithm is tested on different test sequences involving challenging tracking scenarios such as fast motion and their success rates are calculated in Table I. the proposed algorithm works favourably against both algorithms in terms of robustness and success rate.

关键词： Tracking (position)

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An Optimal Underwater image Enhancement based on Fuzzy Gray World Algorithm and Bacterial Foraging Algorithm 5

An Optimal Underwater Image Enhancement based on Fuzzy Gray ...

引用

5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Sethi, Rajni Sreedevi, Indu Verma, O. P. Jain, Veni Delhi Technol Univ CSE Dept Delhi India Delhi Technol Univ ECE Dept Delhi India CPA Global Noida Uttar Pradesh India

ISBN: (纸本)9781467385640

Underwater images suffer from non uniform contrast and poor visibility due to bad illumination and color cast in deep water. Such images have a hazy and color diminished appearance making underwater studies a difficult task. Researches in last decades performed color correction, assuming that underwater images have bluish color cast which is not always true. In this paper, a new image enhancement approach is proposed which modifies the gray world algorithm by finding the color cast using fuzzy logic and then removing the color cast by optimizing the correction method using Bacterial Foraging Optimisation (BFO). Proposed approach is adaptive in nature as it finds the intensity of color cast instead of assuming it which improves the quality of underwater images. Computed results have enhanced visual details, contrast and color performance.

关键词： image Enhancement Fuzzy logic Gray World Assumption Multiobjective BFO Underwater images Color correction

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REAL TIME ANOMALY DETECTION IN H.264 COMPRESSED VIDEOS

REAL TIME ANOMALY DETECTION IN H.264 COMPRESSED VIDEOS

引用

4th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Biswas, Sovan Babu, R. Venkatesh Indian Inst Sci Supercomp Educ Res Ctr Video Analyt Lab Bangalore 560012 Karnataka India

ISBN: (纸本)9781479915880

Real time anomaly detection is the need of the hour for any security applications. In this paper, we have proposed a real-time anomaly detection algorithm by utilizing cues from the motion vectors in H.264/AVC compressed domain. the discussed work is principally motivated by the observation that motion vectors (MVs) exhibit different characteristics during anomaly. We have observed that H.264 motion vector magnitude contains relevant information which can be used to model the usual behavior (UB) effectively. this is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. Additionally, we have suggested a hierarchical approach through Motion Pyramid for High Resolution videos to further increase the detection rate. the proposed algorithm has performed extremely well on UMN and Peds Anomaly Detection Video datasets, with a detection speed of > 150 and 65 - 75 frames per sec in respective datasets resulting in more than 200x speedup along with comparable accuracy to pixel domain state-of-the-art algorithms.

关键词： Anomaly Detection H.264/AVC Compressed Domain Real Time Detection Video Surveillance

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Generic Action Recognition from Egocentric Videos 5

Generic Action Recognition from Egocentric Videos

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Singh, Suriya Arora, Chetan Jawahar, C. V. IIIT Hyderabad CVIT Hyderabad Andhra Pradesh India IIIT Delhi New Delhi India

ISBN: (纸本)9781467385640

Egocentric cameras are wearable cameras mounted on a person's head or shoulder. With their ability to have first person view, such cameras are spawning new set of exciting applications in computer vision. Recognising activity of the wearer from an egocentric video is an important but challenging problem. the task is made especially difficult because of unavailability of wearer's pose as well as extreme camera shake due to motion of wearer's head. Solutions suggested so far for the problem, have either focussed on short term actions such as pour, stir etc. or long term activities such as walking, driving etc. the features used in both the styles are very different and the technique developed for one style often fail miserably on other kind. In this paper we propose a technique to identify if a long term or a short term action is present in an egocentric video segment. this allows us to have a generic first-person action recognition system where we can recognise both short term as well as long term actions of the wearer. We report an accuracy of 90.15% for our classifier on publicly available egocentric video dataset comprising 18 hours of video amounting to 1.9 million tested samples.

关键词： Cameras

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Single image dehazing using image boundary constraint and nearest neighborhood optimization 11

Single image dehazing using image boundary constraint and ne...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Gautam, Sidharth Gandhi, Tapan Kumar Panigrahi, B. K. Indian Inst Technol New Delhi India

ISBN: (纸本)9781450366151

Single image dehazing is an ill-posed problem that requires assumptions, priors and constraints to solve. In this paper, boundary constraint utilizing median filter has been proposed on the image radiance for the rough estimation of transmission-map in haze images. Furthermore, for the refinement of estimated transmission-map, the proposed method use a multi-dimensional feature space that uses nearest neighborhood optimization under the framework of non-local principle. Experimental results manifest that proposed method is effective and results in visually appealing dehaze images that can be useful for subsequent user or computer based application.

关键词： Median filters

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3D Face reconstruction from hard blended edges 15

3D Face reconstruction from hard blended edges

引用

作者： Ding, Yueming Mok, P.Y. The Hong Kong Polytechnic University Inst. Textiles and Clothing Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong

ISBN: (纸本)9789898704320

3D face reconstruction from 2D images is an important research topic because it supports a wide range of applications, such as face recognition, animations, games, and AR/VR systems. 3D face reconstruction from contour features is a challenging task, because traditional edge detection algorithms produce a lot of noises, which are prone to making the reconstruction model trapped in a local optimum or even being degraded. With the development of deep learning, a lot of researcher introduce neural network into contour detection, which can extract relatively clear contours compared with previous methods. In this article, we employ a hard blended face contour feature from neural network and canny edge extractor for face reconstruction. Our method not only improves the 3D face model reconstruction accuracy on synthesis images, but performs more accurately and robustly on in-the-wild images under blurriness, makeup, occlusion and ill-illumination conditions. © 2021 International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： image enhancement

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Shape Recognition based on Shape-Signature Identification and Condensibility: Application to Underwater imagery

Shape Recognition based on Shape-Signature Identification an...

引用

4th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Banerjee, Jeet Ray, Ranjit Vadali, Siva Ram Krishna Layek, Ritwik Kumar Shome, Sankar Nath Indian Inst Technol Elect & Elect Commun Dept Kharagpur 721302 W Bengal India CSIR Cent Mech Engn Res Inst Robot & Automat Div Durgapur 713209 W Bengal India

ISBN: (纸本)9781479915880

In this paper, a shape recognition method is proposed for a few common geometrical shapes including straight line, circle, ellipse, triangle, quadrilateral, pentagon and hexagon. In the present work, two indices namely Unique Shape Signature (USS) and Condensibility (C) are employed for shape recognition of an object. Using the USS index, all the above mentioned non-circular shapes are neatly recognized, whereas, the C index recognized the circular objects. An added advantage of the proposed method is that it can further differentiate triangles, quadrilaterals and both symmetric and non-symmetric shapes of pentagon and hexagon using distance variance (Var(d(si))) parameter calculated from USS. Applying the proposed method on above mentioned shapes, an overall recognition rate of 98.80% is achieved on several simulated and real objects of different shapes. Proposed method has also been compared with two existing methods, presents better result. Performance of the proposed method is illustrated by applying it on underwater images and it is observed to perform satisfactory on all the images under test.

关键词： C (programming language)

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Compensating for Large In-Plane Rotations in Natural images 16

Compensating for Large In-Plane Rotations in Natural Images

引用

10th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Boominathan, Lokesh Srinivas, Suraj Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Bangalore 560012 Karnataka India

ISBN: (纸本)9781450347532

Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. this is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. this is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical 'unrotated' images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical 'unrotated' image. third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.

关键词： Deep neural networks image retrieval rotation invariance Bayesian optimization

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Diagnosis of Pomegranate Plant Diseases using Neural Network 5

Diagnosis of Pomegranate Plant Diseases using Neural Network

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5th National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Dhakate, Mrunmayee Ingole, A. B. SPPU Sinhgad Acad Engn Dept E&TC Pune Maharashtra India

ISBN: (纸本)9781467385640

Pomegranate is a fruit which grows with a very high yield in many states of India and one of the most profits gaining fruit in the market. But due to various conditions, the plants are infected by various diseases which destroy the entire crop leaving very less product yield. So, the work proposes an image processing and neural network methods to deal with the main issues of phytopathology i.e. disease detection and classification. the Pomegranate fruit as well as the leaves are affected by various diseases caused by fungus, bacteria and the climatic conditions. these diseases are like Bacterial Blight, Fruit Spot, Fruit rot and Leaf spot. the system uses some images for training, some for testing purpose and so on. the color images are pre-processed and undergo k-means clustering segmentation. the texture features are extracted using GLCM method, and given to the artificial neural network. the overall accuracy of this method is 90%. the results are proved to be accurate and satisfactory in contrast to manual grading and hopefully take a strong rise in establishing itself in the market as one of the most efficient process.

关键词： disease detection and classification pomegranate plant diseases k-means clustering segmentation GLCM method artificial neural network

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