In this paper, we present a novel approach for the adaptation of large images to small display sizes. As a recent
study suggests, most viewers prefer the loss of content over the insertion of deformations in the retargeting
process.1 Therefore, we combine the two image retargeting operators seam carving and cropping in order to
resize an image without manipulating the important objects in an image at all. First, seams are removed carefully
until a dynamic energy threshold is reached to prevent the creation of visible artifacts. Then, a cropping window
is selected in the image that has the smallest possible window size without having the removed energy rise above
a second dynamic threshold. As the number of removed seams and the size of the cropping window are not fix,
the process is repeated iteratively until the target size is reached. Our results show that by using this method,
more important content of an image can be included in the cropping window than in normal cropping. The
"squeezing" of objects which might occur in approaches based on warping or scaling is also prevented.
In order to display a high dynamic range (HDR) video on a regular low dynamic range (LDR) screen, it needs
to be tone mapped. A great number of tone mapping (TM) operators exist - most of them designed to tone
map one image at a time. Using them on each frame of an HDR video individually leads to flicker in the
resulting sequence. In our work, we analyze three tone mapping operators with respect to flicker. We propose
a criterion for the automatic detection of image flicker by analyzing the log average pixel brightness of the tone
mapped frame. Flicker is detected if the difference between the averages of two consecutive frames is larger
than a threshold derived from Stevens' power law. Fine-tuning of the threshold is done in a subjective study.
Additionally, we propose a generic method to reduce flicker as a post processing step. It is applicable to all tone
mapping operators. We begin by tone mapping a frame with the chosen operator. If the flicker detection reports
a visible variation in the frame's brightness, its brightness is adjusted. As a result, the brightness variation is
smoothed over several frames, becoming less disturbing.
In this paper, we propose a new method to adapt the resolution of images to the limited display resolution
of mobile devices. We use the seam carving technique to identify and remove less relevant content in images.
Seam carving achieves a high adaptation quality for landscape images and distortions caused by the removal of
seams are very low compared to other techniques like scaling or cropping. However, if an image depicts objects
with straight lines or regular patterns like buildings, the visual quality of the adapted images is much lower.
Errors caused by seam carving are especially obvious if straight lines become curved or disconnected. In order
to preserve straight lines, our algorithm applies line detection in addition to the normal energy function of seam
carving. The energy in the local neighborhood of the intersection point of a seam and a straight line is increased
to prevent other seams from removing adjacent pixels. We evaluate our improved seam carving algorithm and
compare the results with regular seam carving. In case of landscape images with no straight lines, traditional
seam carving and our enhanced approach lead to very similar results. However, in the case of objects with
straight lines, the quality of our results is significantly better.
We enhance an existing in-circuit, inline tester for printed circuit assemblies (PCA) by video-based automatic optical
inspection (Video-AOI). Our definition of video is that we continuously capture images of a moving PCA, such that each
PCA component is contained in multiple images, taken under varying viewing conditions like angle, time, camera settings
or lighting. This can then be exploited for an efficient detection of faults. The first part of our paper focuses on the
parameters of such a Video-AOI system and shows how they can be determined. In the second part, we introduce techniques
to capture and preprocess a video of a PCA, so that it can be used for inspection.
In this paper, we introduce our new visualization service which presents web pages and images on arbitrary devices with
differing display resolutions. We analyze the layout of a web page and simplify its structure and formatting rules. The
small screen of a mobile device is used much better this way. Our new image adaptation service combines several
techniques. In a first step, border regions which do not contain relevant semantic content are identified. Cropping is used
to remove these regions. Attention objects are identified in a second step. We use face detection, text detection and
contrast based saliency maps to identify these objects and combine them into a region of interest. Optionally, the seam
carving technique can be used to remove inner parts of an image. Additionally, we have developed a software tool to
validate, add, delete, or modify all automatically extracted data. This tool also simulates different mobile devices, so that
the user gets a feeling of how an adapted web page will look like. We have performed user studies to evaluate our web
and image adaptation approach. Questions regarding software ergonomics, quality of the adapted content, and perceived
benefit of the adaptation were asked.