In this paper, we present a method for temporal propagation of depth data that is available for so called key-frames
through video sequence. Our method requires that full frame depth information is assigned. Our method utilizes nearest
preceding and nearest following key-frames with known depth information. The propagation of depth information from
two sides is essential as it allows to solve most occlusion problems correctly. Image matching is based on the coherency
sensitive hashing (CSH) method and is done using image pyramids. Disclosed results are compared with temporal
interpolation based on motion vectors from optical flow algorithm. The proposed algorithm keeps sharp depth edges of
objects even in situations with fast motion or occlusions. It also handles well many situations, when the depth edges
don’t perfectly correspond with true edges of objects.
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.
KEYWORDS: Image segmentation, Video, Image processing, Digital filtering, Video surveillance, Nonlinear filtering, Algorithm development, Color difference, Data acquisition, Image filtering
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.
KEYWORDS: Printing, Biomimetics, Edge detection, Image enhancement, Visual process modeling, Human vision and color perception, Detection and tracking algorithms, Raster graphics, Image processing, Color printing
Saving of toner/ink consumption is an important task in modern printing devices. It has a positive ecological and social
impact. We propose technique for converting print-job pictures to a recognizable and pleasant color sketches. Drawing a
"pencil sketch" from a photo relates to a special area in image processing and computer graphics - non-photorealistic
rendering. We describe a new approach for automatic sketch generation which allows to create well-recognizable
sketches and to preserve partly colors of the initial picture. Our sketches contain significantly less color dots then initial
images and this helps to save toner/ink. Our bio-inspired approach is based on sophisticated edge detection technique for
a mask creation and multiplication of source image with increased contrast by this mask. To construct the mask we use
DoG edge detection, which is a result of blending of initial image with its blurred copy through the alpha-channel, which
is created from Saliency Map according to Pre-attentive Human Vision model. Measurement of percentage of saved
toner and user study proves effectiveness of proposed technique for toner saving in eco-friendly printing mode.
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
answers.
The paper relates to a method for effective reduction of artifacts, caused by lossy compression algorithms based on
block-based discreet cosine transform (DCT) coding, known as JPEG coding. Most common artifacts produced by such
type of coding, are blocking and ringing artifacts. To reduce the effect of coding artifacts caused by significant
information loss, a variety of different algorithms and methods has been suggested. However, the majority of solutions
propose to process all blocks in the image, which leads to increase of processing time, required resources, as well as
image over-blurring after processing of blocks, not affected by blocking artifacts. Techniques for ringing artifact
detection usually rely on edge-detection step, a complicated and versatile procedure with unknown optimal parameters.
In this paper we describe very effective procedures for detection of artifacts, and their subsequent correction. This
approach helps to save notable amount of computational resources, since not all the blocks are involved in correction
procedures. Detection steps are performed in frequency domain, using only DCT coefficients of an image. Numerous
examples have been analyzed and compared with the existent solutions, and results prove the effectiveness of proposed
technique.
In this paper we propose an effective approach for creating nice-looking photo images of scenes having high dynamic
range using a set of photos captured with exposure bracketing. Usually details of dark parts of the scene are preserved in
over-exposed shot, and details of brightly illuminated parts are visible in under-exposed photos. A proposed method
allows preservation of those details by first constructing gradient field, mapping it with special function and then
integrating it to restore lightness values using Poisson equation. Resulting image can be printed or displayed on
conventional displays.
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