Real-time content-aware video retargeting on the Android platform for tunnel vision assistance

As mobile devices continue to rise in popularity, advances in overall mobile device processing power lead to further expansion of their capabilities. This, coupled with the fact that many people suffer from low vision, leaves substantial room for advancing mobile development for low vision assistance. Computer vision is capable of assisting and accommodating individuals with blind spots or tunnel vision by extracting the necessary information and presenting it to the user in a manner they are able to visualize. Such a system would enable individuals with low vision to function with greater ease. Additionally, offering assistance on a mobile platform allows greater access. The objective of this thesis is to develop a computer vision application for low vision assistance on the Android mobile device platform. Specifically, the goal of the application is to reduce the effects tunnel vision inflicts on individuals. This is accomplished by providing an in-depth real-time video retargeting model that builds upon previous works and applications. Seam carving is a content-aware retargeting operator which defines 8-connected paths, or seams, of pixels. The optimality of these seams is based on a specific energy function. Discrete removal of these seams permits changes in the aspect ratio while simultaneously preserving important regions. The video retargeting model incorporates spatial and temporal considerations to provide effective image and video retargeting. Data reduction techniques are utilized in order to generate an efficient model. Additionally, a minimalistic multi-operator approach is constructed to diminish the disadvantages experienced by individual operators. In the event automated techniques fail, interactive options are provided that allow for user intervention. Evaluation of the application and its video retargeting model is based on its comparison to existing standard algorithms and its ability to extend itself to real-time. Performance metrics are obtained for both PC environments and mobile device platforms for comparison

Content Aware Video Retargeting using Seam Carving

Video retargeting method achieves high - quality resizing to arbitrary aspect ratios for complex videos containing diverse camera and dynamic motions . Video retargeting from a full - resolution video to a lower resolution display will inevitably cause information loss. While retargeting the video the important contents must also be preserved. Seam carving works well for images without straight lines or regular patterns like landscape images but may cause distortions if used for images with straight lines. Our approach combines Seam Carving method along with Hough transform to preserve the origi nality of the video

Seam Cerving and Salient Detection for Thumbnail Photos

Image resizing is a process of processing images or images with the aim of changing the size of the image. The most commonly used methods are cropping or scaling. Scaling is changing the size of the image based on the scale. Contents in the image are not considered in scaling. Seam carving often uses energy functionality that is useful as a determinant of the pixel level contained in an image. Seam is a connecting path of image pixels both vertically and horizontally that is passed by a low energy function. Changing the image size using seam carving is considered better than cropping and scaling. However, the seam carving method still cannot protect the object that is considered the most important. In overcoming this weakness, we can use a combination of seam carving algorithm with salient detection. In this research, we will improve the two methods which function as thumbnail maker. The results of the salient detection of the most important areas of the image will be detected and as a reference in resizing the image (seam carving) The dataset uses 200 images. The accuracy value is calculated by distributing questionnaires to 100 respondents and producing an acceptance rate of 78% so that the results are Very Natural/Natural

Panoramic Image Communication for Mobile Application using Content-Aware Image Resizing Method

This paper presents an image resizing application for mobile communication to evaluate content-aware image resizing method for panoramic image. In many applications, we can take account into aspect ratio changing, removal or pan and zoom in the image. However, the implemented application in this work is more focus on image downsizing due to mobile application that is limited for image capacity. The generated panoramic image will be distorted if simply scaling by factors and the image will lose information or generate artifacts if crop the area directly. It is meaningful to discuss how to keep the main object in the image and resize the image by cutting off the unnecessary part. The implemented approach has been successfully developed and it will be valuable to compare image resizing on mobile terminal

Image resizing using saliency strength map and seam carving for white blood cell analysis

<p>Abstract</p> <p>Background</p> <p>A new image-resizing method using seam carving and a Saliency Strength Map (SSM) is proposed to preserve important contents, such as white blood cells included in blood cell images.</p> <p>Methods</p> <p>To apply seam carving to cell images, a SSM is initially generated using a visual attention model and the structural properties of white blood cells are then used to create an energy map for seam carving. As a result, the energy map maximizes the energies of the white blood cells, while minimizing the energies of the red blood cells and background. Thus, the use of a SSM allows the proposed method to reduce the image size efficiently, while preserving the important white blood cells.</p> <p>Results</p> <p>Experimental results using the PSNR (Peak Signal-to-Noise Ratio) and ROD (Ratio of Distortion) of blood cell images confirm that the proposed method is able to produce better resizing results than conventional methods, as the seam carving is performed based on an SSM and energy map.</p> <p>Conclusions</p> <p>For further improvement, a faster medical image resizing method is currently being investigated to reduce the computation time, while maintaining the same image quality.</p

Panoramic Image Communication for Mobile Application using Content-Aware Image Resizing Method

This paper presents an image resizing application for mobile communication to evaluate content-aware image resizing method for panoramic image. In many applications, we can take account into aspect ratio changing, removal or pan and zoom in the image. However, the implemented application in this work is more focus on image downsizing due to mobile application that is limited for image capacity. The generated panoramic image will be distorted if simply scaling by factors and the image will lose information or generate artifacts if crop the area directly. It is meaningful to discuss how to keep the main object in the image and resize the image by cutting off the unnecessary part. The implemented approach has been successfully developed and it will be valuable to compare image resizing on mobile terminal

Distortion Sensitive Algorithm to Preserve Line Structure Properties in Image Resampling

In order to remove less important content from image seam carving algorithm is used. In seam carving distortion is very low as compared to other techniques like scaling and cropping. The major drawback of seam carving is when seam intersects with straight line present in the image it distorts line structure; the line may become curve after distortion. This structure distortion not only degrades visual quality of image but also gives artifacts or aliased line structure. This paper presents a content aware seam carving algorithm to resize the image. After applying algorithm discussed the structure of regular objects present in the image can be preserved. In the proposed algorithm first line detection algorithm is applied over the image in order to detect possible straight lines present in the image. After detecting straight lines algorithm tries to find out intersection point of optimal seam with the straight line. Algorithm increases energy of local neighbourhood pixels of intersection point up to a predefined radius, so that no further seam can intersect same pixel again