Keypoint Approach Research Articles

Image Forgery Detection using AKAZE Keypoint Feature Extraction and Trie Matching

Image Forgery is an illegal activity in the society as per cyber laws. There are various types of forgeries in which forgery on images is considered as an illegal activity. Image forgery may take place in different ways. One way for doing forgery on images is copy and move forgery which may result in loss of image integrity or authenticity. There are number of popular detection techniques exist such as SIFT, SURF etc., but have high complexity in detection of forgery. Here we have proposed a method to detect the forgery on images which results in loss of integrity or authenticity. In our proposed method we have used descriptor matching using Trie Data Structure The descriptor matching method of implementation using Trie data structure made the complexity of the problem to reduce to O (n log n). Using Key points approach we can verify the integrity of the image. Extracting the features with key points approach is computational expensive task. But there is KAZE method which overcomes this situation. KAZE’s method of using non-linear diffusion filtering requires it to solve a series of PDEs. This cannot be done analytically forcing KAZE to use a numerical method called an AOS scheme to solve the PDEs. However, this process is computationally costly and therefore an accelerated version of KAZE was created. The Accelerated KAZE or AKAZE which creates non-linear scale space through Fast Explicit Diffusion for reduce the complexity in extracting the features.

An Improved Discrete Cosine Transformation Block Based Scheme for Copy-move Image Forgery Detection

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF.

Global shape modeling using the OSIRIS-REx scanning Laser Altimeter

The OSIRIS-REx Laser Altimeter (OLA) is a scanning lidar instrument with a high measurement rate of 10 kHz and a dexterous two-axis mirror. OLA is optimized for proximity operations (< 7 km range) around a small asteroid. The high repetition rate and scanning capability of the instrument give OLA the ability to rapidly obtain high-density topographic scans of the surface of the near-Earth asteroid (101955) Bennu.We describe our approach to creating a global shape model using overlapping high-fidelity raster scans of the asteroid surface, demonstrated here with simulated data in advance of the asteroid encounter. This approach minimizes the dependence of the shape model on absolute position and pointing knowledge while taking advantage of the excellent short-term relative pointing stability of the instrument. It also provides a computationally efficient method of dealing with a dataset consisting of ≈1 billion data points through the use of keypoints and rigid transformations. The keypoint approach, in which matching features in overlapping scans are assigned a direct correspondence, mitigates against issues such as convergence into local minima, or dependence on initial conditions that methods such as ICP have. We explain how we generate maps, digital elevation models, and digital terrain models for determining the sampleability and safety of potential sample sites and for the scientific interpretation of the asteroid.

An Improved DCT Block Based Technique for Copy-Move Forgery Detection in Medical Images

Copy-Moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The most commonly used technique is the block based which divides image into blocks during the stage of image pre-processing before features are extracted whereas key point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. The proposed technique is based on DCT and an improvement on DCT technique is achieved in terms of dimensionality reduction using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy. Based on the analysis of this work as compared to previous proposed works, since previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the octagonal block was observed as the improvement over the previously proposed dimensionality reduction using the circle block.

A Survey on Keypoint Based Copy-paste Forgery Detection Techniques

Copy-Move forgery is the most common image tampering method to create forged images. The images may be forged to conceal or change the meaning of the photographs. Hence, it becomes important to verify the integrity and authenticity of the images. The copy-move forgery detection can be classified under two heads viz., block based and keypoint based. The block based methods use mostly the similar kind of frameworks but differ in applying feature extraction schemes. The block-based methods are good at detecting the forged regions with high accuracy but is having tremendously high computational complexity. In this paper we review the keypoint approach which is an alternative to block-based approach. The keypoint based copy-move forgery detection schemes involve, detecting and describing the local features of the images by using the algorithms like SIFT and SURF.

비디오 행동 인식을 위하여 다중 판별 결과 융합을 통한 성능 개선에 관한 연구

최근 다양한 방송 및 영상 분야에서 사람의 행동을 인식하여는 연구들이 많이 이루어지고 있다. 영상은 다양한 형태를 가질 수 있기 때문에 제약된 환경에서 유용한 템플릿 방법들보다 특징점에 기반한 연구들이 실제 사용자 환경에서 더욱 관심을 받고 있다. 특징점 기반의 연구들은 영상에서 움직임이 발생하는 지점들을 찾아내어 이를 3차원 패치들로 생성한다. 이를 이용하여 영상의 움직임을 히스토그램에 기반한 descriptor(서술자)로 표현하고 학습기반의 판별기로 최종적으로 영상내에 존재하는 행동들을 인식하였다. 그러나 단일 판별기로는 다양한 행동을 인식하기에 어려움이 있다. 따라서 이러한 문제를 개선하기 위하여 최근에 다중 판별기를 활용한 연구들이 영상 판별 및 물체 검출 영역에서 사용되고 있다. 따라서 본 논문에서는 행동 인식을 위하여 support vector machine과 sparse representation을 이용한 decision-level fusion 방법을 제안하고자 한다. 제안된 논문의 방법은 영상에서 특징점 기반의 descriptor를 추출하고 이를 각각의 판별기를 통하여 판별 결과들을 획득한다. 이 후 학습단계에서 획득된 가중치를 활용하여 각 결과들을 융합하여 최종 결과를 도출하였다. 본 논문에 실험에서 제안된 방법은 기존의 융합 방법보다 높은 행동 인식 성능을 보여 주었다. Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

Social Learning Instead of Educating the Other

Michael Bilharz and Katharina Schmitt (2011) raise serious concerns about the emerging trend to believe in sustainable consumption as a way to contribute to a more sustainable development and about the limited impact of the Lifestyle of Health and Sustainability (LOHAS) trend. They argue that it will need a new strategy for sustainable consumption, in order to change consumer beliefs and habits. Accordingly Bilharz and Schmitt present three alternatives for a more promising communication approach: 1. promoting small matters, 2. playing down small matters and 3. promoting big matters. As they argue, it is the last option of a key point approach that they favour and consequently they introduce principles for communicating big matters.This response does not aim to discuss these principles in detail but rather to challenge some of the underlying assumptions of communication as a way to change beliefs and behaviours. A first important aspect has recently been pointed out by Grunwald (2010) and has started a lively discussion in GAIA (Bilharz et al. 2011; Grunwald 2011; Siebenhuner 2011). Without repeating Grunwald’s argument, I want at least to adopt a critical perspective on the emphasis of individual and private actions for sustainable development. Besides, there are two major concerns I want to raise: the first refers to an understanding of communication as a way to transfer information, while the second critically examines the understanding of communication as a tool for behaviour change.

Going Big with Big Matters. The Key Points Approach to Sustainable Consumption

Sustainable consumption becomes increasingly important for solving sustainability problems: it can empower people to a conscious lifestyle and can pave the way for a sustainability-orientated policy making. But it is not sufficient to consume ecologically friendly products while neglecting those measures with a high environmental impact. To concentrate on so-called key points could therefore be a promising strategy for sustainability communication – but it cannot replace fundamental changes in our political frameworks.