Multi-Scale Image Analysis by Pyramidal Algorithms

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

In the real world, the biological visual system is more efficient than the machine visual system in analyzing visual information. Physiology theories show that this efficiency owes to the multi-layer neural network in human visual system, in which every layer accomplishes different tasks and is related with other layers. The low-level stages of the human visual system, especially the retina, can provide certain scale information for the high-level stages of visual system through using the non-classical receptive field (nCRF) mechanism. This mechanism that the nCRF size can be adjusted automatically by ganglion cell (GC) can achieve a multi-scale image analysis. The results, reflecting the distribution of the image information, can be shared by several algorithms or processes solving different visual tasks, such as contour detection and image segmentation. A model of multi-scale image analysis based on GC has been proposed in this paper, which retains the key information and reduces the redundancy information for the further stages of the visual system. Experimental results on N-cut and contour detection show that this multi-scale image analysis model provides distinctive improvement for these image processing tasks.

Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.

Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages of a segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are − 14.88 , − 16.93 , − 16.96 and − 16.87 respectively, and the overall accuracy achieves 92 % after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020.

Subsurface penetrating radar (SPR) fullwaveform inversion (FWI) is an emerging near-surface high-resolution imaging technology that quantitatively evaluate the permittivity of the subsurface. However, because echo contains limited information in SPR FWI, the difficulty to obtain accurate inversion result increases. In this work, we develop a FWI algorithm for SPR to improve the performance of SPR FWI. In the optimization, the image segmentation is employed to correct the update direction. Hybrid clustering algorithm including Canopy and K-means were utilized for segmentation. The numerical experiment demonstrates the dynamic image segmentation improve the performance of FWI.

Towards intelligent robust detection of anatomical structures in incomplete volumetric data.

Urban is composed of different critical levels of organization where interactions are stronger within levels than among levels, it is a complex system. Image analysis performed at a unique scale for urban applications is doomed to be incomplete and misleading. This paper presents a multiscale image analysis method based on wavelet transform and watershed transform for urban system. It performs image analysis on multiple scale images, and can catch comprehensive information of urban system for different applications. The procedure consists of three main steps: multiscale representation, image segmentation, post-processing. First, multiscale represent creates multiresolution images using a wavelet transform and a modified inverse wavelet transform; a smooth and multiscale presentation of original image is provided. Second, image segmentation segments the multiscale images using watershed segment algorithm. Finally, post-processing merges over-segment regions and smoothes watersheds, then, multiscale image objects are attained. This study was conducted in Nanjing, Jiangsu province, China, using IKONOS satellite data. Experimental results demonstrate that the proposed method is suitable for describing the complex urban system. Both visual and quantitative methods are applied to evaluation the experimental results, and reveal the relation of objects within and among levels.

Precision agriculture is finding its roots in India. PA always deals with the accuracy and timely information about agriculture products. With the rapid development of computer hardware and software technology, the application of image processing technology in the agricultural research are playing key role [1]. Also, with the advantages of superior speed and accuracy, computer vision has attracted it as an alternative to human inspection [2]. In this paper, we have described a novel application of computer vision and color image segmentation for automating the precise yield prediction process of gerbera flower yield from the polyhouse images. The purpose of the present study is to design a decision support system that could generate flower yield information and serve as base for management & planning of flower marketing. Current study has applied the color image segmentation technique using threshold, to extract the flowers from the scene. Color is considered a fundamental physical property of agriculture products and foods in information analysis [3]. Using HSV color space and histogram analysis, flower color definition is done. Then by the image segmentation process, flowers were separated from the background & detected in the images. Image set with 75 images were tested with this technique.

Expert Systems for Image Processing, Analysis, and Recognition: Declarative Knowledge Representation for Computer Vision

The image of biometric properties in humans is used in many fields today. Regardless of these features, it is necessary to first translate it into data that the computer understands. In this study, automatic and dynamic image segmentation was performed by using 300x300 fingerprint images. A fingerprint database with a total of 80 images and 10 different classes was used. The features of the images were subtracted from the sub-segments obtained from these images by the feature extraction algorithm that was originally developed. The 300x300 images were divided into 25x25 sub-images and the feature vector was obtained. 144x80 inputs obtained after image segmentation were kept in areas in separate tables. The developed segmentation and feature extraction algorithm can be applied to any image of equal size.

EPT: An R package for ensemble patch transform

This paper proposes a method of multiscale image analysis through an operator called the operator. By considering an image function as a surface function, the surface-shape operator describes shape of each pixel in terms of the topographic structure such as hill, dale, ridge, valley, etc. The surface-shape operator is used as a pre-processing for extracting the multiscale topographic structures of images in scale-space representation. Then, image features are extracted from the transformed images instead of the original ones. The surface-shape operator has invariant properties under monotonic and linear gray tone transformations, and is insensitive to additive noise modelled by linear functions, so the proposed method has a robustness with brightness variations and shading effects. We illustrate the usefulness of the proposed method with an application to image classification, and also demonstrate performances concerning the robustness with brightness variations and shading effects. The multiresolution simultaneous autoregressive (MRSAR) method is selected as a representative method of multiscale approaches for image classification used to-date for comparison purposes. Results show that the proposed method performs better and has much more robustness with brightness variations and shading effects than the MRSAR method.

Chapter 88 - Image Processing Analytics: Enhancements and Segmentation

– Medical image segmentation is considered the most precarious element in the analysis and processing of real-life images in the clinical sector. Actually, segmentation effects affect the subsequent procedures of image evaluation, life object illustration and description, feature dimensions and the subsequent considerable task levels such as object categorization. In that case, medical image segmentation is the most essential and crucial aspect for aiding the visualization, delineation and depiction of regions of interest for any particular image. The aspect of physical segmentation of a picture is not just challenging and time-consuming task to do, but also problematic to assure accuracy considering the wide-range image modalities and unguided image quantities which have to be observed. In that case, it now becomes fundamental to evaluate the present approaches of image segmentation based on the application of computing algorithms which require the interaction of users with evaluating medical images. In the process of image segmentation, an anatomic classification requires to be extracted or defined to be projected effectively and independently. In that case, this contribution is focused on image segmentation to extract details for decision-making in the clinical sector. The paper presents generalized and relative techniques that have been categorized into three groups: pixel-centre, edge-centred and region-centred techniques. The paper also provides a highlight of the strengths and weaknesses of these techniques in reference to the appropriateness of a wide-range application in medical image segmentation.

Image segmentation is crucial for multimedia applications. Multimedia databases utilize segmentation for the storage and indexing of images and video. Image segmentation is used for object tracking in the new MPEG-7 video compression standard. It is also used in video conferencing for compression and coding purposes. These are only some of the multimedia applications in image segmentation. It is usually the first task of any image analysis process, and thus, subsequent tasks rely heavily on the quality of segmentation. The proposed method of color image segmentation is very effective in segmenting a multimedia-type image into regions. Pixels are first classified as either chromatic or achromatic depending on their HSI color values. Next, a seed determination algorithm finds seed pixels that are in the center of regions. These seed pixels are used in the region growing step to grow regions by comparing these seed pixels to neighboring pixels using the cylindrical distance metric. Merging regions that are similar in color is a final means used for segmenting the image into even smaller regions.