Proc. SPIE. 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015)
KEYWORDS: Convolution, Digital filtering, Image processing, Image filtering, Image enhancement, Infinite impulse response filters, Digital signal processing, Filtering (signal processing), Very large scale integration, Field programmable gate arrays
In this paper we describe a Hardware Accelerator (HWA) for fast recursive approximation of separable convolution with exponential function. This filter can be used in many Image Processing (IP) applications, e.g. depth-dependent image blur, image enhancement and disparity estimation. We have adopted this filter RTL implementation to provide maximum throughput in constrains of required memory bandwidth and hardware resources to provide a power-efficient VLSI implementation.
In this article we propose high quality motion estimation based on variational optical flow formulation with non-local
regularization term. To improve motion in occlusion areas we introduce occlusion motion inpainting based on 3-frame
motion clustering. Variational formulation of optical flow proved itself to be very successful, however a global
optimization of cost function can be time consuming. To achieve acceptable computation times we adapted the algorithm
that optimizes convex function in coarse-to-fine pyramid strategy and is suitable for modern GPU hardware
implementation. We also introduced two simplifications of cost function that significantly decrease computation time
with acceptable decrease of quality. For motion clustering based motion inpaitning in occlusion areas we introduce
effective method of occlusion aware joint 3-frame motion clustering using RANSAC algorithm. Occlusion areas are
inpainted by motion model taken from cluster that shows consistency in opposite direction. We tested our algorithm on
Middlebury optical flow benchmark, where we scored around 20th position, but being one of the fastest method near the
top. We also successfully used this algorithm in semi-automatic 2D to 3D conversion tool for spatio-temporal
background inpainting, automatic adaptive key frame detection and key points tracking.
Recently applications involving capture of scenes with object of interest among surroundings gained high popularity.
Such applications include video surveillance, human motion capture, human-computer interaction, etc. For proper
analysis of the object of interest a necessary step is to separate the object of interest from surroundings, i. e. perform
background subtraction (or silhouette extraction). This is a challenging task because of several problems, which are
slight changes in background; shadows caused by the object of interest; and similarly colored objects. In this work we
propose a new method for extracting the silhouette of an object of interest, based upon the joint use of both depth (range)
and color data. Depth-based data is independent of color image data, and hence not affected by the limitations associated
with color-based segmentation, such as shadows and similarly colored objects. At the initial moment an image of the
background (not containing the object of interest) is present, and it is updated in every frame taking into account
extracted silhouette and using "running average". Silhouette extraction method is based on k-means clustering of depth
data and color difference data, and per-pixel silhouette mask computation, using clusters' centroids. The proposed
solution is very fast and allows real-time processing of video. Developed algorithm has been successfully applied in
human recognition application and provided good results for modeling human figure.
Modern consumer 3D TV sets are able to show video content in two different modes: 2D and 3D. In 3D mode, stereo
pair comes from external device such as Blue-ray player, satellite receivers etc. The stereo pair is split into left and right
images that are shown one after another. The viewer sees different image for left and right eyes using shutter-glasses
properly synchronized with a 3DTV. Besides, some devices that provide TV with a stereo content are able to display
some additional information by imposing an overlay picture on video content, an On-Screen-Display (OSD) menu. Some
OSDs are not always 3D compatible and lead to incorrect 3D reproduction. In this case, TV set must recognize the type
of OSD, whether it is 3D compatible, and visualize it correctly by either switching off stereo mode, or continue
demonstration of stereo content.
We propose a new stable method for detection of 3D incompatible OSD menus on stereo content. Conventional OSD is a
rectangular area with letters and pictograms. OSD menu can be of different transparency levels and colors. To be 3D
compatible, an OSD is overlaid separately on both images of a stereo pair. The main problem in detecting OSD is to
distinguish whether the color difference is due to OSD presence, or due to stereo parallax. We applied special techniques
to find reliable image difference and additionally used a cue that usually OSD has very implicit geometrical features:
straight parallel lines. The developed algorithm was tested on our video sequences database, with several types of OSD
with different colors and transparency levels overlaid upon video content. Detection quality exceeded 99% of true
Present paper generally relates to content-aware image resizing and image inscribing into particular predetermined areas.
The problem consists in transformation of the image to a new size with or without modification of aspect ratio in a
manner that preserves the recognizability and proportions of the important features of the image. Most close solutions
presented in prior art cover along with standard image linear scaling, including down-sampling and up-sampling, image
cropping, image retargeting, seam carving and some special image manipulations which similar to some kind of image
retouching. Present approach provides a method for digital image retargeting by means of erasing or addition of less
significant image pixels. The defined above retargeting approach can be easily used for image shrinking easily. However,
for image enlargement there are some limitations as a stretching artifact. History map with relaxation is introduced to
avoid such drawback and overcome some known limits of retargeting. In proposed approach means for important objects
preservation are taken into account. It allows significant improvement of resulting quality of retargeting. Retargeting
applications for different devices such as display, copier, facsimile and photo-printer are described as well.