Dynamic Amodal Completion Through the Magic WandIllusion

In Remote sensing systems one of the most important features needed are roads, which require automated procedures to rapidly identify them from high-resolution satellite imagery, Many approaches for automatic road extraction have appeared in literature [2][7][9], which vary due to the differences in their goals, available information, algorithms used and assumptions about roads. In this paper, we propose an approach for automatic road extraction by integrating region and edge information. The complimentary information of road segments obtained using Probabilistic SVM(PSVM) and road edges obtained using Dominant Singular Measure (DSM) are integrated using a modified Constraint Satisfaction Neural Network -Complementary Information Integration(CSNN-CII) [1] to improve the accuracy of the system. Results are shown on real-world images and quantitatively evaluated with manual hand-drawn road layouts.

This paper presents a novel multimodal medical image fusion framework (M3IF-NSST-CNN) combining the Non-Subsampled Shearlet Transform (NSST) and deep convolutional neural network (CNN) methods to enhance diagnostic accuracy by effectively integrating Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Initially, both CT and MRI images undergo decomposition via NSST, producing distinct low-pass (LPSB) and high-pass subbands (HPSB). The LPSB fusion leverages a correlation-based approach, ensuring preservation of structural integrity and maximization of complementary information. Concurrently, the HPSB are merged through a Universal Image Quality Index (UIQI)-based fusion rule, prioritizing edge and detail retention critical for precise medical diagnosis. Subsequently, an inverse NSST transforms these fused sub-bands, generating a preliminary fused image. This image is then further refined through a CNN-based noise reduction module comprising convolutional layers, batch normalization, and Rectified Linear Unit (ReLU) activation functions. The deep CNN architecture progressively refines the fused output, significantly reducing artifacts and noise, thus improving the overall clarity and interpretability of the resultant image. This approach addresses common limitations in image fusion, such as loss of edge details, distortion, and the inability to preserve critical information. The hybrid M3IF-NSST-CNN method improves both the visual quality and parametric accuracy of the fusion, outperforming comparable techniques. By preserving more detail and edge information, this method offers significant advancements in multi-modal medical image fusion for enhanced diagnostic insights.

The usefulness of an image database depends on whether the image of interest can be easily located. Feature extraction is a crucial step of image retrieval. The well-known SIFT descriptor is a keypoint-based image feature. It can be used to robustly find the same objects in different images, i.e. achieve the object recognition task. However, it is not effective on the image retrieval task, i.e. finding images with similar content. To improve the SIFT algorithm, we propose a robust image retrieval algorithm based on the integration of keypoints and edges information. Our approach is robust to translation, rotation and partial occlusion of the object. Experimental results indicate that the proposed algorithm is effective and outperforms the SIFT algorithm.

This paper presents a color recognition and segmentation of color image through a combination of a multi-scale fuzzy system, an image edge based on Prewitt operator and morphological operators. In the proposed algorithm, after converting image from RGB color space to hue-saturation-value (HSV) color space, different scales of fuzzy sets in hue channel is constructed according to the people's psychological cognition to color. The image color would be quickly recognized fuzzily as less than ten kinds on scale one. To improve the performance of the color recognition rate, recognized image need to be identified on scale two to distinguish different colors observed by human eyes with the same color scheme. Then, adopting Prewitt operator to extract image edge information and applying a sequence of some morphological operators to the edge image, completing the multi-scale segmentation of image with the integration of color and edge information. Finally, indicating its area and species of specific color. The proposed algorithm can automatically adjusted in the process of color recognition and segmentation of image, it achieves recognition and segmentation without supervision. The obtained result has a good consistency with man's subjective visual perception.

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach.

In this paper, a new intelligent hardware module suitable for the computation of an adaptive median filter is presented for the first time. The function of the proposed circuit is to detect the existence of impulse noise in an image neighborhood and apply the operator of the median filter only when it is necessary. The noise detection procedure can be controlled so that a range of pixel values is considered as impulse noise. In this way, the blurring of the image in process is avoided, and the integrity of edge and detail information is preserved. Experimental results with real images demonstrate the improved performance. The proposed digital hardware structure is capable to process gray-scale images of 8-bit resolution and is fully pipelined, whereas parallel processing is used in order to minimize computational time. In the presented design, a 3/spl times/3 or 5/spl times/5 pixel image neighborhood can be selected for the computation of the filter output. However, the system can be easily expanded to accommodate windows of larger sizes. The proposed digital structure was designed, compiled and simulated using the MAX+PLUS II Programmable Logic Development System by Altera Corporation. For the implementation of the system the EPF10K200SRC240-1 field-programmable gate array device of the FLEX10KE device family is utilized, and it can be used in industrial imaging applications where fast processing is required. The typical clock frequency is 65 MHz.

Segmentation of hippocampus in the brain is one of a major challenge in medical image segmentation due to its’ imaging characteristics, with almost similar intensity between another adjacent gray matter structure, such as amygdala. The intensity similarity has causes the hippocampus to have weak or fuzzy boundaries. With this main challenge being demonstrated by hippocampus, a segmentation method that relies on image information alone may not produce accurate segmentation results. Therefore, it is needed an assimilation of prior information such as shape and spatial information into existing segmentation method to produce the expected segmentation. Previous studies has widely integrated prior information into segmentation methods. However, the prior information has been utilized through a global manner integration, and this does not reflect the real scenario during clinical delineation. Therefore, in this paper, a locally integrated prior information into a level set model is presented. This work utilizes a mean shape model to provide automatic initialization for level set evolution, and has been integrated as prior information into the level set model. The local integration of edge based information and prior information has been implemented through an edge weighting map that decides at voxel level which information need to be observed during a level set evolution. The edge weighting map shows which corresponding voxels having sufficient edge information. Experiments shows that the proposed integration of prior information locally into a conventional edge-based level set model, known as geodesic active contour has shown improvement of 9% in averaged Dice coefficient.

Global and local information aggregation network for edge-aware salient object detection