Abstract: With the aging of the world population and the
continuous growth in technology, service robots are more and more
explored nowadays as alternatives to healthcare givers or personal
assistants for the elderly or disabled people. Any service robot
should be capable of interacting with the human companion, receive
commands, navigate through the environment, either known or
unknown, and recognize objects. This paper proposes an approach
for object recognition based on the use of depth information and
color images for a service robot. We present a study on two of the
most used methods for object detection, where 3D data is used to
detect the position of objects to classify that are found on horizontal
surfaces. Since most of the objects of interest accessible for service
robots are on these surfaces, the proposed 3D segmentation reduces
the processing time and simplifies the scene for object recognition.
The first approach for object recognition is based on color histograms,
while the second is based on the use of the SIFT and SURF feature
descriptors. We present comparative experimental results obtained
with a real service robot.
Abstract: Although most digital cameras acquire images in a raw
format, based on a Color Filter Array that arranges RGB color
filters on a square grid of photosensors, most image compression
techniques do not use the raw data; instead, they use the rgb result
of an interpolation algorithm of the raw data. This approach is
inefficient and by performing a lossless compression of the raw data,
followed by pixel interpolation, digital cameras could be more power
efficient and provide images with increased resolution given that the
interpolation step could be shifted to an external processing unit. In
this paper, we conduct a survey on the use of lossless compression
algorithms with raw Bayer images. Moreover, in order to reduce the
effect of the transition between colors that increase the entropy of
the raw Bayer image, we split the image into three new images
corresponding to each channel (red, green and blue) and we study
the same compression algorithms applied to each one individually.
This simple pre-processing stage allows an improvement of more than
15% in predictive based methods.