When customers are finished with a laser ink cartridge, they often send it back using the return box provided with new cartridges. These cartridges are returned to centralized recycling centers across the country by the truck loads. Used cartridges are removed from their packaging and thrown onto a conveyor belt for manual sorting. As volumes steadily increase, addtional manpower is required to aid in the sorting. Since the recycling center is credited for each cartridge processed and certain cartridges have higher number of recyclable components then others, accurate count of models is highly desired.
PC-based vision system monitors the conveyor for cartridges as they enter into the field of view, at which point it makes note of the cartridges encoder location and its orientation. Using a blob tool, the system pre-classifies cartridges based on their shape using a high-end multi-core processsor for parallelized pattern matching. Subsequent secondary pattern matching is performed in some instances to identify other key markers when subcategorization is required. A local register retains counts of each cartridge identified. Encoder location, orientation, and cartridge ID are passed to a spider robot to pick and place the cartridges into large bins. Multiple instances and orientations of well over one hundred cartridges were trained into the system.
Animal Health Sciences start-up was seeking a subject matter expert to develop machine vision solution to determine the orientation of a baby chickens head for the application of vaccine.
For this SBIR Phase 1 project, a custom lighting and optical solution was developed to acquire images of baby chicks as they passed under the camera. 1000 images where collected and annotated, 2/3rd of the images were then fed into Deep Learning algrothims. Detection performance was 97% with an execution time of 19-22ms.
After the nail polish bottle has been capped, a consumer label is applied to the top of the cap that indicates the color name and product number. Space constraints prevents an inspection camera to be placed after the label applicator, so the Vision System needs to be able to compensate for 360 orientation of the label.
With some blob morphology techniques the orientation of the text can be determined. Afterwards an OCR tool was applied to read the text twice, oriented 180 degrees apart. If the text found did not match the HMI, the product was rejected.
International mail brought in through airline processing centers are often in large sacks with a 4”x6” card attached to it. Depending on the country of origin, some of these cards are hand written and others are printed. The individual at the processing window needs to manually enter in all of the information on the mail tag into a data entry system before it can be thrown onto a conveyor belt to be processed. This creates a bottle neck in the work flow, since only so many transfer windows can be manned and managed to allow baggage/cargo trucks to be unloaded.
A portable prototype imager with a flat surface allowed the mail handlers to position the mail tag for image acquistion. At the start of a new load the mail handler would enter in the Airline and Flight number, along with the number of pieces. The mail handler would take an image of that tag and then the local Vision PC would attempt to read all of the text on the tag, then present the image along with data to the mail handler. Once approved, the data would be saved to a database. If tag was not readable, the image was sent to a remote location where it was presented to a person for manual entry. This allowed for reconcilation of a load and moving mail bags as quickly as the manual imaging process would allow.
Supplier for the machines that manufacture the Permanent Residency Cards needed to ensure that the text printed on the card matches what is contained in the database. It was also to ensure the person’s face was printed clearly, and verify the presence of several security features.
Standard lighting techniques and Optical Character Recognition routines where used to validate the text and data that was contained in the database. Image correlation was used to validate the person’s face printed on the cards. Multiple different lighting techniques and LED wavelengths were used with different image processing alghrothims to verify the presence of all security features.
Supplier for the machines that manufacture the Driver’s Licenses needed to ensure the text printed on the card matched what is contained in the database. Also needed to ensure the person’s face was printed clearly, and verify presence of the engraved birth date on the card.
Standard lighting techniques and Optical Character Recognition routines where used to validate the text and data that was contained in the database. Image correlation was used to validate the person’s face printed on the cards. Photometric stereo imaging technique with IR LED lights was used to extract the engraving and filter out the print on the driver’s license.