Quality evaluation of agricultural and food products is important for processing, inventory control, and marketing. Fruit size and surface quality are two important quality factors for high-quality fruit such as Medjool dates. Fruit size is usually measured by length that can be done easily by simple image processing techniques. Surface quality evaluation on the other hand requires more complicated design, both in image acquisition and image processing. Skin delamination is considered a major factor that affects fruit quality and its value. This paper presents an efficient histogram analysis and image processing technique that is designed specifically for real-time surface quality evaluation of Medjool dates. This approach, based on short-wave infrared imaging, provides excellent image contrast between the fruit surface and delaminated skin, which allows significant simplification of image processing algorithm and reduction of computational power requirements. The proposed quality grading method requires very simple training procedure to obtain a gray scale image histogram for each quality level. Using histogram comparison, each date is assigned to one of the four quality levels and an optimal threshold is calculated for segmenting skin delamination areas from the fruit surface. The percentage of the fruit surface that has skin delamination can then be calculated for quality evaluation. This method has been implemented and used for commercial production and proven to be efficient and accurate.
There has been significant research on the development of feature descriptors in the past few years. Most of them do not emphasize real-time applications. This paper presents the development of an affine invariant feature descriptor for low resource applications such as UAV and UGV that are equipped with an embedded system with a small microprocessor, a field programmable gate array (FPGA), or a smart phone device. UAV and UGV have proven suitable for many promising applications such as unknown environment exploration, search and rescue operations. These applications required on board image processing for obstacle detection, avoidance and navigation. All these real-time vision applications require a camera to grab images and match features using a feature descriptor. A good feature descriptor will uniquely describe a feature point thus allowing it to be correctly identified and matched with its corresponding feature point in another image. A few feature description algorithms are available for a resource limited system. They either require too much of the device’s resource or too much simplification on the algorithm, which results in reduction in performance. This research is aimed at meeting the needs of these systems without sacrificing accuracy. This paper introduces a new feature descriptor called PRObabilistic model (PRO) for UGV navigation applications. It is a compact and efficient binary descriptor that is hardware-friendly and easy for implementation.
In general, fruits and vegetables such as tomatoes and dates are harvested before they fully ripen. After harvesting, they continue to ripen and their color changes. Color is a good indicator of fruit maturity. For example, tomatoes change color from dark green to light green and then pink, light red, and dark red. Assessing tomato maturity helps maximize its shelf life. Color is used to determine the length of time the tomatoes can be transported. Medjool dates change color from green to yellow, and the orange, light red and dark red. Assessing date maturity helps determine the length of drying process to help ripen the dates. Color evaluation is an important step in the processing and inventory control of fruits and vegetables that directly affects profitability. This paper presents an efficient color back projection and image processing technique that is designed specifically for real-time maturity evaluation of fruits. This color processing method requires very simple training procedure to obtain the frequencies of colors that appear in each maturity stage. This color statistics is used to back project colors to predefined color indexes. Fruit maturity is then evaluated by analyzing the reprojected color indexes. This method has been implemented and used for commercial production.
Hundreds of millions of people use hand-held devices frequently and control them by touching the screen with their
fingers. If this method of operation is being used by people who are driving, the probability of deaths and accidents
occurring substantially increases. With a non-contact control interface, people do not need to touch the screen. As a
result, people will not need to pay as much attention to their phones and thus drive more safely than they would
otherwise. This interface can be achieved with real-time stereovision. A novel Intensity Profile Shape-Matching
Algorithm is able to obtain 3-D information from a pair of stereo images in real time. While this algorithm does have a
trade-off between accuracy and processing speed, the result of this algorithm proves the accuracy is sufficient for the
practical use of recognizing human poses and finger movement tracking. By choosing an interval of disparity, an object
at a certain distance range can be segmented. In other words, we detect the object by its distance to the cameras. The
advantage of this profile shape-matching algorithm is that detection of correspondences relies on the shape of profile and
not on intensity values, which are subjected to lighting variations. Based on the resulting 3-D information, the
movement of fingers in space from a specific distance can be determined. Finger location and movement can then be
analyzed for non-contact control of hand-held devices.
Proc. SPIE. 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
KEYWORDS: Target detection, Unmanned aerial vehicles, Detection and tracking algorithms, Cameras, Sensors, Image segmentation, Control systems, Sensor fusion, Global Positioning System, RGB color model
In recent years, autonomous, micro-unmanned aerial vehicles (micro-UAVs), or more specifically hovering micro-
UAVs, have proven suitable for many promising applications such as unknown environment exploration and search
and rescue operations. The early versions of UAVs had no on-board control capabilities, and were difficult for
manual control from a ground station. Many UAVs now are equipped with on-board control systems that reduce the
amount of control required from the ground-station operator. However, the limitations on payload, power
consumption and control without human interference remain the biggest challenges.
This paper proposes to use a smartphone as the sole computational device to stabilize and control a quad-rotor.
The goal is to use the readily available sensors in a smartphone such as the GPS, the accelerometer, the rate-gyros,
and the camera to support vision-related tasks such as flight stabilization, estimation of the height above ground,
target tracking, obstacle detection, and surveillance. We use a quad-rotor platform that has been built in the Robotic
Vision Lab at Brigham Young University for our development and experiments. An Android smartphone is
connected through the USB port to an external hardware that has a microprocessor and circuitries to generate pulse-width
modulation signals to control the brushless servomotors on the quad-rotor. The high-resolution camera on the
smartphone is used to detect and track features to maintain a desired altitude level. The vision algorithms
implemented include template matching, Harris feature detector, RANSAC similarity-constrained homography, and
color segmentation. Other sensors are used to control yaw, pitch, and roll of the quad-rotor. This smartphone-based
system is able to stabilize and control micro-UAVs and is ideal for micro-UAVs that have size, weight, and power