scholarly journals Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Kangho Paek ◽  
Min Yao ◽  
Zhongwei Liu ◽  
Hun Kim
Keyword(s):  
Image Pyramid ◽  
Image Patch ◽  
Keypoint Detection ◽  
Binary Descriptor ◽  
Image Patches ◽  
High Resolution Images ◽  
Matching Performance ◽  
Computer Vision Applications ◽  
Patch Matching ◽  
Sampling Pattern

Matching of keypoints across image patches forms the basis of computer vision applications, such as object detection, recognition, and tracking in real-world images. Most of keypoint methods are mainly used to match the high-resolution images, which always utilize an image pyramid for multiscale keypoint detection. In this paper, we propose a novel keypoint method to improve the matching performance of image patches with the low-resolution and small size. The location, scale, and orientation of keypoints are directly estimated from an original image patch using a Log-Spiral sampling pattern for keypoint detection without consideration of image pyramid. A Log-Spiral sampling pattern for keypoint description and two bit-generated functions are designed for generating a binary descriptor. Extensive experiments show that the proposed method is more effective and robust than existing binary-based methods for image patch matching.

scholarly journals LEARNING THE 3D POSE OF VEHICLES FROM 2D VEHICLE PATCHES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 683-688
Author(s):  
C. Koetsier ◽  
T. Peters ◽  
M. Sester
Keyword(s):  
Elevation Angle ◽  
Training Data ◽  
Training Dataset ◽  
Image Patch ◽  
Movement Trajectories ◽  
Surveillance Camera ◽  
Image Patches ◽  
Ground Point ◽  
Precise Movement ◽  
Real Time Applications

Abstract. Estimating vehicle poses is crucial for generating precise movement trajectories from (surveillance) camera data. Additionally for real time applications this task has to be solved in an efficient way. In this paper we introduce a deep convolutional neural network for pose estimation of vehicles from image patches. For a given 2D image patch our approach estimates the 2D coordinates of the image representing the exact center ground point (cx, cy) and the orientation of the vehicle - represented by the elevation angle (e) of the camera with respect to the vehicle’s center ground point and the azimuth rotation (a) of the vehicle with respect to the camera. To train a accurate model a large and diverse training dataset is needed. Collecting and labeling such large amount of data is very time consuming and expensive. Due to the lack of a sufficient amount of training data we show furthermore, that also rendered 3D vehicle models with artificial generated textures are nearly adequate for training.

scholarly journals Bipolar Log-Intensity-Variance Histogram Method for Local Image Patch Intensity Change Measurements

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Han Wang ◽  
Quan Shi ◽  
Zhihuo Xu ◽  
Ming Wei ◽  
Hanseok Ko
Keyword(s):  
Intensity Change ◽  
Image Patch ◽  
Fixed Position ◽  
Histogram Method ◽  
Probabilistic Measure ◽  
Object Movement ◽  
Image Patches ◽  
Intensity Changes ◽  
Sigmoid Functions ◽  
Local Image

For a fixed-position camera, the intensity changes of an image pixel are often caused by object movement or illumination change. This paper focuses on such a problem: given two adjacent local image patches, how can the causes of intensity change be determined? A bipolar log-intensity-variance histogram is proposed to describe the intensity variations on the chaos phase plot subspace. This is combined with two sigmoid functions to construct a probabilistic measure function. Experimental results show that the proposed measurements are more effective and robust than conventional methods to the cause of variation in image intensity.

Multi-relation Attention Network for Image Patch Matching

2021 ◽  
pp. 1-1
Author(s):  
Dou Quan ◽  
Shuang Wang ◽  
Yi Li ◽  
Bowu Yang ◽  
Ning Huyan ◽  
...  
Keyword(s):  
Image Patch ◽  
Attention Network ◽  
Patch Matching

scholarly journals Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6235
Author(s):  
Chengyi Xu ◽  
Ying Liu ◽  
Fenglong Ding ◽  
Zilong Zhuang
Keyword(s):  
Feature Matching ◽  
Recognition Rate ◽  
Image Features ◽  
Geometric Feature ◽  
Feature Descriptor ◽  
Image Patch ◽  
Feature Description ◽  
Point Pair ◽  
Image Patches ◽  
Local Image

Considering the difficult problem of robot recognition and grasping in the scenario of disorderly stacked wooden planks, a recognition and positioning method based on local image features and point pair geometric features is proposed here and we define a local patch point pair feature. First, we used self-developed scanning equipment to collect images of wood boards and a robot to drive a RGB-D camera to collect images of disorderly stacked wooden planks. The image patches cut from these images were input to a convolutional autoencoder to train and obtain a local texture feature descriptor that is robust to changes in perspective. Then, the small image patches around the point pairs of the plank model are extracted, and input into the trained encoder to obtain the feature vector of the image patch, combining the point pair geometric feature information to form a feature description code expressing the characteristics of the plank. After that, the robot drives the RGB-D camera to collect the local image patches of the point pairs in the area to be grasped in the scene of the stacked wooden planks, also obtaining the feature description code of the wooden planks to be grasped. Finally, through the process of point pair feature matching, pose voting and clustering, the pose of the plank to be grasped is determined. The robot grasping experiment here shows that both the recognition rate and grasping success rate of planks are high, reaching 95.3% and 93.8%, respectively. Compared with the traditional point pair feature method (PPF) and other methods, the method present here has obvious advantages and can be applied to stacked wood plank grasping environments.

scholarly journals Zebra Crossing Detection and Time Scheduling Accuracy, Enhancement Optimization Using Artificial Intelligence

2021 ◽  
pp. 515-525
Author(s):  
Pratik Kumar Sinha ◽  
Dr. Sujesh D. Ghodmare
Keyword(s):  
Regression Model ◽  
Recall Performance ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Image Patch ◽  
Fundamental Function ◽  
The Road ◽  
Image Patches ◽  
Regression Approach ◽  
Time Scheduling

Zebra crossing detection is a fundamental function of the electronic travel aid. It can locate the zebra crossing and estimate its direction to help the visually impaired to cross the road safely. In contrast to the conventional methods, a regression approach is adopted to detect zebra crossing based on convolutional neural networks. Specifically, a fixed‐size window slides across the image captured at the intersection. The image patches are sequentially fed to the logistic regression model to identify the zebra crossing. Then the image patch of zebra crossing is fed to the regression model to predict the direction. The parameters of models are optimized by the ANN back propagation algorithm before predictions. Compared with existing methods, the proposed method can improve the precision‐recall performance of the zebra crossing identification and reduce the root mean square error of predicted directions.

scholarly journals Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

2021 ◽  
Vol 15 ◽  
Author(s):  
Qiuzhuo Liu ◽  
Yaqin Luo ◽  
Ke Li ◽  
Wenfeng Li ◽  
Yi Chai ◽  
...  
Keyword(s):  
Image Restoration ◽  
Physical Model ◽  
State Of The Art ◽  
The State ◽  
Depth Information ◽  
Single Image ◽  
Image Patch ◽  
Image Defogging ◽  
Image Patches ◽  
Scene Depth

Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document